Relevant bibliographies by topics / Euclidean networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Euclidean networks'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Euclidean networks"

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Xuan, Qi, Xiaodi Ma, Chenbo Fu, Hui Dong, Guijun Zhang, and Li Yu. "Heterogeneous multidimensional scaling for complex networks." International Journal of Modern Physics C 26, no. 02 (February 2015): 1550023. http://dx.doi.org/10.1142/s0129183115500230.

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Many real-world networks are essentially heterogeneous, where the nodes have different abilities to gain connections. Such networks are difficult to be embedded into low-dimensional Euclidean space if we ignore the heterogeneity and treat all the nodes equally. In this paper, based on a newly defined heterogeneous distance and a generalized network distance under the constraints of network and triangle inequalities, respectively, we propose a new heterogeneous multidimensional scaling method (HMDS) to embed different networks into proper Euclidean spaces. We find that HMDS behaves much better than the traditional multidimensional scaling method (MDS) in embedding different artificial and real-world networks into Euclidean spaces. Besides, we also propose a method to estimate the appropriate dimensions of Euclidean spaces for different networks, and find that the estimated dimensions are quite close to the real dimensions for those geometrical networks under study. These methods thus can help to better understand the evolution of real-world networks, and have practical importance in network visualization, community detection, link prediction and localization of wireless sensors.
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Xing, Chenjie, Yuan Zhou, Yinan Peng, Jieke Hao, and Shuoshi Li. "Specific Emitter Identification Based on Ensemble Neural Network and Signal Graph." Applied Sciences 12, no. 11 (May 28, 2022): 5496. http://dx.doi.org/10.3390/app12115496.

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Specific emitter identification (SEI) is a technology for extracting fingerprint features from a signal and identifying the emitter. In this paper, the author proposes an SEI method based on ensemble neural networks (ENN) and signal graphs, with the following innovations: First, a signal graph is used to show signal data in a non-Euclidean space. Namely, sequence signal data is constructed into a signal graph to transform the sequence signal from a Euclidian space to a non-Euclidean space. Hence, the graph feature (the feature of the non-Euclidean space) of the signal can be extracted from the signal graph. Second, the ensemble neural network is integrated with a graph feature extractor and a sequence feature extractor, making it available to extract both graph and sequence simultaneously. This ensemble neural network also fuses graph features with sequence features, obtaining an ensemble feature that has both features in Euclidean space and non-Euclidean space. Therefore, the ensemble feature contains more effective information for the identification of the emitter. The study results demonstrate that this SEI method has higher SEI accuracy and robustness than traditional machine learning methods and common deep learning methods.
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Huang, Shao-Lun, Changho Suh, and Lizhong Zheng. "Euclidean Information Theory of Networks." IEEE Transactions on Information Theory 61, no. 12 (December 2015): 6795–814. http://dx.doi.org/10.1109/tit.2015.2484066.

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Carlsson, John Gunnar, and Fan Jia. "Euclidean Hub-and-Spoke Networks." Operations Research 61, no. 6 (December 2013): 1360–82. http://dx.doi.org/10.1287/opre.2013.1219.

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Wu, Wei, Guangmin Hu, and Fucai Yu. "An Unsupervised Learning Method for Attributed Network Based on Non-Euclidean Geometry." Symmetry 13, no. 5 (May 19, 2021): 905. http://dx.doi.org/10.3390/sym13050905.

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Many real-world networks can be modeled as attributed networks, where nodes are affiliated with attributes. When we implement attributed network embedding, we need to face two types of heterogeneous information, namely, structural information and attribute information. The structural information of undirected networks is usually expressed as a symmetric adjacency matrix. Network embedding learning is to utilize the above information to learn the vector representations of nodes in the network. How to integrate these two types of heterogeneous information to improve the performance of network embedding is a challenge. Most of the current approaches embed the networks in Euclidean spaces, but the networks themselves are non-Euclidean. As a consequence, the geometric differences between the embedded space and the underlying space of the network will affect the performance of the network embedding. According to the non-Euclidean geometry of networks, this paper proposes an attributed network embedding framework based on hyperbolic geometry and the Ricci curvature, namely, RHAE. Our method consists of two modules: (1) the first module is an autoencoder module in which each layer is provided with a network information aggregation layer based on the Ricci curvature and an embedding layer based on hyperbolic geometry; (2) the second module is a skip-gram module in which the random walk is based on the Ricci curvature. These two modules are based on non-Euclidean geometry, but they fuse the topology information and attribute information in the network from different angles. Experimental results on some benchmark datasets show that our approach outperforms the baselines.
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Xu, Xinzheng, Xiaoyang Zhao, Meng Wei, and Zhongnian Li. "A comprehensive review of graph convolutional networks: approaches and applications." Electronic Research Archive 31, no. 7 (2023): 4185–215. http://dx.doi.org/10.3934/era.2023213.

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<abstract> <p>Convolutional neural networks (CNNs) utilize local translation invariance in the Euclidean domain and have remarkable achievements in computer vision tasks. However, there are many data types with non-Euclidean structures, such as social networks, chemical molecules, knowledge graphs, etc., which are crucial to real-world applications. The graph convolutional neural network (GCN), as a derivative of CNNs for non-Euclidean data, was established for non-Euclidean graph data. In this paper, we mainly survey the progress of GCNs and introduce in detail several basic models based on GCNs. First, we review the challenges in building GCNs, including large-scale graph data, directed graphs and multi-scale graph tasks. Also, we briefly discuss some applications of GCNs, including computer vision, transportation networks and other fields. Furthermore, we point out some open issues and highlight some future research trends for GCNs.</p> </abstract>
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Liang, Fan, Cheng Qian, Wei Yu, David Griffith, and Nada Golmie. "Survey of Graph Neural Networks and Applications." Wireless Communications and Mobile Computing 2022 (July 28, 2022): 1–18. http://dx.doi.org/10.1155/2022/9261537.

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The advance of deep learning has shown great potential in applications (speech, image, and video classification). In these applications, deep learning models are trained by datasets in Euclidean space with fixed dimensions and sequences. Nonetheless, the rapidly increasing demands on analyzing datasets in non-Euclidean space require additional research. Generally speaking, finding the relationships of elements in datasets and representing such relationships as weighted graphs consisting of vertices and edges is a viable way of analyzing datasets in non-Euclidean space. However, analyzing the weighted graph-based dataset is a challenging problem in existing deep learning models. To address this issue, graph neural networks (GNNs) leverage spectral and spatial strategies to extend and implement convolution operations in non-Euclidean space. Based on graph theory, a number of enhanced GNNs are proposed to deal with non-Euclidean datasets. In this study, we first review the artificial neural networks and GNNs. We then present ways to extend deep learning models to deal with datasets in non-Euclidean space and introduce the GNN-based approaches based on spectral and spatial strategies. Furthermore, we discuss some typical Internet of Things (IoT) applications that employ spectral and spatial convolution strategies, followed by the limitations of GNNs in the current stage.
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Gao, Baojian, Xiaoning Zhao, Jun Wang, and Xiaojiang Chen. "Decomposition Based Localization for Anisotropic Sensor Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/805061.

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Range-free localization algorithms have caused widespread attention due to their low cost and low power consumption. However, such schemes heavily depend on the assumption that the hop count distance between two nodes correlates well with their Euclidean distance, which will be satisfied only in isotropic networks. When the network is anisotropic, holes or obstacles will lead to the estimated distance between nodes deviating from their Euclidean distance, causing a serious decline in localization accuracy. This paper develops HCD-DV-Hop for node localization in anisotropic sensor networks. HCD-DV-Hop consists of two steps. Firstly, an anisotropic network is decomposed into several different isotropic subnetworks, by using the proposed Hop Count Based Decomposition (HCD) scheme. Secondly, DV-Hop algorithm is carried out in each subnetwork for node localization. HCD first uses concave/convex node recognition algorithm and cleansing criterion to obtain the optimal concave and convex nodes based on boundary recognition, followed by segmentation of the network’s boundary. Finally, the neighboring boundary nodes of the optimal concave nodes flood the network with decomposition messages; thus, an anisotropic network is decomposed. Extensive simulations demonstrated that, compared with range-free DV-Hop algorithm, HCD-DV-Hop can effectively reduce localization error in anisotropic networks without increasing the complexity of the algorithm.
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Trietsch, Dan. "Augmenting Euclidean Networks—the Steiner Case." SIAM Journal on Applied Mathematics 45, no. 5 (October 1985): 855–60. http://dx.doi.org/10.1137/0145051.

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Kartun-Giles, Alexander, Suhanya Jayaprakasam, and Sunwoo Kim. "Euclidean Matchings in Ultra-Dense Networks." IEEE Communications Letters 22, no. 6 (June 2018): 1216–19. http://dx.doi.org/10.1109/lcomm.2018.2799207.

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Dissertations / Theses on the topic "Euclidean networks"

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Grafström, Amanda. "Hur biodiversitet på ekosystemnivå skiljer sig mellan olika habitat." Thesis, Linköpings universitet, Institutionen för fysik, kemi och biologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108429.

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Biodiversity can be described as the total variation of life forms, where diversity ranges from gene level up to the ecosystem level. The diversity can be calculated in a number of ways, and this study use one of these methods. In this study empirical food webs have been used and analyzed, where eleven characters are defined and used as parameters to calculate the Euclidean distances between food webs that describe the variation that may exist within classes of terrestrial, marine and freshwater habitats. The class who stood out and showed the greatest diversity at the ecosystem level was the marine food webs, which showed a high value of the average euclidean distance. The other networks were not as distinctive and the average of the euclidean distance in these classes was comparatively low.
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Huang, Shao-Lun Ph D. Massachusetts Institute of Technology. "Euclidean network information theory." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84888.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 121-123).
Many network information theory problems face the similar difficulty of single letterization. We argue that this is due to the lack of a geometric structure on the space of probability distributions. In this thesis, we develop such a structure by assuming that the distributions of interest are all close to each other. Under this assumption, the Kullback-Leibler (K-L) divergence is reduced to the squared Euclidean metric in an Euclidean space. In addition, we construct the notion of coordinate and inner product, which will facilitate solving communication problems. We will present the application of this approach to the point-to-point channels, general broadcast channels (BC), multiple access channels (MAC) with common sources, interference channels, and multi-hop layered communication networks without or with feedback. It can be shown that with this approach, information theory problems, such as the single-letterization, can be reduced to some linear algebra problems. Solving these linear algebra problems, we will show that for the general broadcast channels, transmitting the common message to receivers can be formulated as the trade-off between linear systems. We also provide an example to visualize this trade-off in a geometric way. For the MAC with common sources, we observe a coherent combining gain due to the cooperation between transmitters, and this gain can be obtained quantitively by applying our technique. In addition, the developments of the broadcast channels and multiple access channels suggest a trade-off relation between generating common messages for multiple users and transmitting them as the common sources to exploit the coherent combining gain, when optimizing the throughputs of communication networks. To study the structure of this trade-off and understand its role in optimizing the network throughput, we construct a deterministic model by our local approach that captures the critical channel parameters and well models the network. With this deterministic model, for multi-hop layered networks, we analyze the optimal network throughputs, and illustrate what kinds of common messages should be generated to achieve the optimal throughputs. Our results provide the insight of how users in a network should cooperate with each other to transmit information efficiently.
by Shao-Lun Huang.
Ph.D.
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Gomes, Tânia Tenório. "Rede ARTMAP Euclidiana utilizada na solução do problema de previsão de cargas elétricas." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/152580.

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Submitted by TÂNIA TENÓRIO GOMES (taniatgs@yahoo.com.br) on 2018-01-23T18:19:50Z No. of bitstreams: 1 Tania_dissertação_dee_22_12_2017.pdf: 2284683 bytes, checksum: 58edd8d14052f9f162a3d091782c28c3 (MD5)
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Made available in DSpace on 2018-01-24T11:18:07Z (GMT). No. of bitstreams: 1 gomes_tt_me_ilha.pdf: 2284683 bytes, checksum: 58edd8d14052f9f162a3d091782c28c3 (MD5) Previous issue date: 2017-12-22
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
A geração e distribuição de energia elétrica fazem parte de um vasto esquema no setor elétrico de cada país, tornando-se cada vez mais necessário criar alternativas para minimizar seu custo. Realizar a previsão de cargas elétricas de forma precisa garante uma infraestrutura mais eficiente e confiável para planejamento e operação do sistema elétrico. A proposta deste trabalho é realizar a previsão de carga elétrica global a curto prazo, utilizando uma técnica que forneça uma boa precisão, seja confiável e de baixo custo computacional. Portanto, foi utilizada a rede neural artificial ARTMAP Euclidiana, que é baseada na Teoria da Ressonância Adaptativa. Com objetivo de analisar a eficiência da metodologia proposta foram realizados 3 casos com diferentes horizontes de treinamento, sendo calculado o erro percentual médio. Os dados utilizados para as simulações são de uma companhia elétrica espanhola. O principal objetivo deste trabalho é aplicar a rede neural ARTMAP Euclidiana na previsão de cargas elétricas 24 horas à frente e para validar e verificar se esta rede é uma boa ferramenta para este tipo de estudo foi utilizada a rede neural ARTMAP Fuzzy com os mesmos dados empregados na rede ARTMAP Euclidiana como critério de comparação para comprovar a eficiência da rede neural ARTMAP Euclidiana.
Generation and distribution of electrical energy are very important for the development of the countries and it is necessary to create alternatives to minimize the costs. Electrical load forecasting must be realized precisely to assure a reliable and secure operation of the electrical system. The proposal of this work is to realize the short term global electrical load forecasting using a technique with good precision and reliable and with low computational cost. Thus, the Euclidian ARTMAP neural network was used which is also based on the adaptive resonance theory. Three different cases with different horizons were used for training and the percentual error is calculated. The data are from a Spanish company. The main objective is to apply the Euclidian ARTMAP neural network to forecast the loads 24 hours ahead. The results are compared with the traditional Fuzzy ARTMAP neural network using the same data and the comparison is effectuated evaluating the MAPE (mean absolute percentual error).
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Bassi, Regiane Denise Solgon. "Identicação inteligente de patologias no trato vocal." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-14032014-080118/.

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Com base em exames como a videolaringoscopia, que é considerado um procedimento médico invasivo e desconfortável, diagnósticos têmsido realizados visando detectar patologias na laringe. Geralmente, esse tipo de exame é realizado somente com solicitação médica e quando alterações na fala já são marcantes, ou há sensação de dor. Nessa fase, muitas vezes a doença está em grau avançado, dificultando o seu tratamento. Com o objetivo de realizar um pré-diagnóstico computacional de tais patologias, este trabalho apresenta uma técnica não invasiva na qual são testados e comparados três classificadores: a Distância Euclidiana, a Rede Neural RBF com o kernel Gaussiano e a Rede Neural RBF com o kernel Gaussiano modificado. Testes realizados com uma base de dados de vozes normais e aquelas afetadas por diversas patologias demonstram a eficácia da técnica proposta, que pode, inclusive, ser implementada em tempo-real.
Based on examinations such as laryngoscopy, which is considered an invasive and uncomfortable procedure, diagnosis have been performed aiming at the detection of larynx pathologies. Usually, this type of test is carried out upon medical request and when the speech changes are notable or are causing pain. At this point, the disease is possibly at an advanced degree, complicating its treatment. In order to perform a computational pre-diagnosis of such conditions, this work proposes a noninvasive technique in which three classifiers are tested and compared: the Euclidean distance, the RBF Neural Network with the Gaussian kernel and RBF Neural Network with a modified Gaussian kernel. Tests carried out with a database of normal voices and those affected by various pathologies demonstrate the effectiveness of the technique that may even be implemented to work in real time.
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Eriksson, Björn. "Biodiversity at the ecosystem level : structural variation among food webs in temperate and tropical areas." Thesis, Linköpings universitet, Biologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108120.

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Biodiversity is a fundamental part of the functioning of ecosystems and their ability to provide ecosystem services. It has been shown that a high biodiversity increases the robustness of an ecosystem according to the insurance hypothesis. I propose that a similar effect can be seen on a higher scale, where a high diversity of ecosystem types might stabilize the ecological functionality of a region. By comparing eleven network characters in 70 tropical and temperate ecosystems, their diversity was measured as Euclidean distance between the systems in the 11-dimensional room defined by these characters.  The diversity of ecosystems was shown to be significantly higher in tropical latitudes than in temperate. A possible explanation to this result could be that the higher species diversity in the tropics allows for more types of ecosystems. A higher diversity of ecosystems in a region might indicate a larger amount and variation of possible ecosystem goods and services as well as provide the region with an increased robustness. The measurement of ecosystem diversity between regions might also be of importance in a conservation perspective, where unique and vulnerable ecosystems can be discovered and protected.
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Kharchenko, Natalia. "Lattice algorithms and lattice-based cryptography." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS337.

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La cryptographie basée sur les réseaux est un domaine de recherche qui étudie la construction d'outils pour une communication sécurisée basée sur des problèmes de réseaux difficiles. La cryptographie basée sur les réseau est l'un des candidats les plus prometteurs pour la communication sécurisée post-quantique. Cette thèse étudie les algorithmes pour résoudre les problèmes de réseaux difficiles et leur application à l'évaluation de la sécurité des constructions cryptographiques. Dans la première partie, nous introduisons une nouvelle famille d'algorithmes de sieving appelé sieving cylindrique. Le sieving heuristique est actuellement l'approche la plus rapide pour résoudre les problèmes de réseau central, SVP et CVP. Nous montrons que le sieving cylindrique peut surpasser les algorithmes de sieving existants dans certains cas, à savoir qu'il est plus efficace pour résoudre SVP pour des réseaux avec un volume premier petit et pour résoudre le problème de vecteur le plus proche avec prétraitement (CVPP). Dans la deuxième partie de la thèse, nous améliorons l'attaque duale utilisée à l'origine pour estimer la sécurité du Fast Fully Homomorphic Encryption scheme over Torus (TFHE). Nous hybridons l'attaque duale avec la recherche de la partie de la clé secrète. Comme TFHE utilise des clés binaires, la partie recherche de l'attaque peut être effectuée efficacement en exploitant la structure récursive de l'espace de recherche. Nous comparons notre attaque avec d'autres techniques existantes pour résoudre LWE et montrons que le niveau de sécurité du schéma TFHE devrait être mis à jour par rapport à la nouvelle attaque
Lattice-based cryptography is a field of research that studies the construction of tools for secure communication based on hard lattice problems. Lattice-based cryptography is one of the most promising candidates for secure post-quantum communication. This thesis studies algorithms for solving hard lattice problems and their application to the evaluation of the security of cryptosystems. In the first part, we introduce a new family of lattice sieving algorithms called cylindrical sieving. Heuristic sieving is currently the fastest approach to solve central lattice problems: SVP and CVP. We show that cylindrical sieving can outperform existing sieving algorithms in some cases, namely, that it is more efficient for solving SVP for lattices with small prime volume and for solving the closest vector problem with preprocessing (CVPP). In the second part of the thesis, we improve the dual attack originally used to estimate the security of the Fast Fully Homomorphic Encryption scheme over Torus (TFHE). We hybridize the dual attack with the search for the secret key part. As TFHE uses binary keys, the search part of the attack can be performed efficiently by exploiting the recursive structure of the search space. We compare our attack with other existing techniques for solving LWE and show that the security level of the TFHE scheme should be updated according to the new attack
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Collet, François. "Short scale study of 4-simplex assembly with curvature, in euclidean Loop Quantum Gravity." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4076/document.

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Une étude d'un assemblage symétrique de trois 4-simplex en géométrie classique, de Regge et quantique. Nous étudions les propriétés géométriques et surtout la présence de courbure. Nous montrons que les géométries classique et de Regge de l'assemblage ont une courbure qui évolue en fonction de ses paramètres de bordure. Pour la géométrie quantique, une version euclidienne du modèle EPRL est utilisé avec une valeur pratique du paramètre Barbero-Immirzi pour définir l'amplitude de transition de l'ensemble et de ses composants. Un code C ++ est conçu pour calculer les amplitudes et étudier numériquement la géométrie quantique. Nous montrons qu'une géométrie classique, avec une courbure, émerge déjà à bas spin. Nous reconnaissons également l'apparition de configurations dégénérées et de leurs effets sur la géométrie attendue
A study of symmetrical assembly of three euclidean 4-simplices in classical, Regge and quantum geometry. We study the geometric properties and especially the presence of curvature. We show that classical and Regge geometry of the assembly have curvature which evolves in function of its boundary parameters. For the quantum geometry, a euclidean version of EPRL model is used with a convenient value of the Barbero-Immirzi parameter to define the transition amplitude of the assembly and its components. A C++ code is design for compute the amplitudes and study numerically the quantum geometry. We show that a classical geometry, with curvature, emerges already at low spin. We also recognize the appearance of the degenerate configurations and their effects on the expected geometry
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Shepherd, Matthew. "Green Space Access in Scottish Cities : GIS Analysis of Accessibility in Scotland's Four Largest Cities." Thesis, Umeå universitet, Institutionen för geografi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-159783.

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This study looks at the difference in accessibility to green spaces within the four largest Scottish cities. Having access to green spaces provides several physical and mental health benefits while also providing important ecosystem services. Previous studies show that the frequency of use of a green space declines once the distance surpasses 300 m to an access point. Geographic Information Systems (GIS) were used to analyse the service area of an access point to a green space, from which the rate of accessibility is established. The study also analyses the difference in accessibility between Euclidean and network distance. It is found that the Euclidean difference underestimates the distance needed to reach an access point and that 300 m recommendation by Euclidean distance is more closely resembles 500 m network distance. This study recommends that a distinction be made between which measurement metric is used when stating distances regarding accessibility, in order to create a more consistent approach.
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Baghaee, Sajjad. "Identification And Localization On A Wireless Magnetic Sensor Network." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614447/index.pdf.

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This study focused on using magnetic sensors for localization and identification of targets with a wireless sensor network (WSN). A wireless sensor network with MICAz motes was set up utilizing a centralized tree-based system. The MTS310, which is equipped with a 2-axis magnetic sensor was used as the sensor board on MICAz motes. The use of magnetic sensors in wireless sensor networks is a topic that has gained limited attention in comparison to that of other sensors. Research has generally focused on the detection of large ferromagnetic targets (e.g., cars and airplanes). Moreover, the changes in the magnetic field intensity measured by the sensor have been used to obtain simple information, such as target direction or whether or not the target has passed a certain point. This work aims at understanding the sensing limitations of magnetic sensors by considering small-scale targets moving within a 30 cm radius. Four heavy iron bars were used as test targets in this study. Target detection, identification and sequential localization were accomplished using the Minimum Euclidean Distance (MED) method. The results show the accuracy of this method for this job. Different forms of sensor sensing region discretization were considered. Target identification was done on the boundaries of sensing regions. Different gateways were selected as entrance point for identification point and the results of them were compared with each other. An online ILS system was implemented and continuous movements of the ferromagnetic objects were monitored. The undesirable factors which affect the measurements were discussed and techniques to reduce or eliminate faulty measurements are presented. A magnetic sensor orientation detector and set/reset strap have been designed and fabricated. Orthogonal Matching Pursuit (OMP) algorithm was proposed for multiple sensors multiple target case in ILS systems as a future work. This study can then be used to design energy-efficient, intelligent magnetic sensor networks
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Ibrahim, Amin Abdurahman. "Detecting and preventing the electronic transmission of illicit images." Thesis, UOIT, 2009. http://hdl.handle.net/10155/23.

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The sexual exploitation of children remains a very serious problem and is rapidly increasing globally through the use of the Internet. This work focuses on the current methods employed by criminals to generate and distribute child pornography, the methods used by law enforcement agencies to deter them, and the drawbacks of currently used methods, as well as the surrounding legal and privacy issues. A proven method to detect the transmission of illicit images at the network layer is presented within this paper. With this research, it is now possible to actively filter illicit pornographic images as they are transmitted over the network layer in real-time. It is shown that a Stochastic Learning Weak Estimator learning algorithm and a Maximum Likelihood Estimator learning algorithm can be applied against Linear Classifiers to identify and filter illicit pornographic images. In this thesis, these two learning algorithms were combined with algorithms such as the Non-negative Vector Similarity Coefficient-based Distance algorithm, Euclidian Distance, and Weighted Euclidian Distance. Based upon this research, a prototype was developed using the abovementioned system, capable of performing classification on both compressed and uncompressed images. Experimental results showed that classification accuracies and the overhead of network-based approaches did have a significant effect on routing devices. All images used in our experiments were legal. No actual child pornography images were ever collected, seen, sought, or used.
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Books on the topic "Euclidean networks"

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Henderson, Andrea. Algebra. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198809982.003.0003.

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The difference between the transcendent Coleridgean symbol and the unreliable conventional symbol was of explicit concern in Victorian mathematics, where the former was aligned with Euclidean geometry and the latter with algebra. Rather than trying to bridge this divide, practitioners of modern algebra and the pioneers of symbolic logic made it the founding principle of their work. Regarding the content of claims as a matter of “indifference,” they concerned themselves solely with the formal interrelations of the symbolic systems devised to represent those claims. In its celebration of artificial algorithmic structures, symbolic logician Lewis Carroll’s Sylvie and Bruno dramatizes the power of this new formalist ideal not only to revitalize the moribund field of Aristotelian logic but also to redeem symbolism itself, conceived by Carroll and his mathematical, philosophical, and symbolist contemporaries as a set of harmonious associative networks rather than singular organic correspondences.
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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Specific constructions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0009.

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This chapter presents network-generating models which cannot be neatly categorized as growing, nor as defined primarily through a target degree distribution. They are best understood as mechanistic constructions designed to elucidate a particular feature of the network. In the first sub-section, the Watts–Strogatz model is introduced and motivated as a construction to achieve both a high degree of clustering and a low average path length. Geometric graphs, in their Euclidian flavour, are shown to be a natural choice for broadcast networks. The Hyperbolic variant is informally described, because it is known to be a natural space in which to embed hierarchical graphs. Planar graphs have very specific real-world applications, but are extraordinarily challenging to analyze mathematically. Finally, weighted graphs allow for concepts such as traffic to be incorporated into the random graph model.
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Book chapters on the topic "Euclidean networks"

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de Souza, Renata M. C. R., and Telmo de M. Silva Filho. "Optimized Learning Vector Quantization Classifier with an Adaptive Euclidean Distance." In Artificial Neural Networks – ICANN 2009, 799–806. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04274-4_82.

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Lee, Sanghwan, Zhi-Li Zhang, Sambit Sahu, Debanjan Saha, and Mukund Srinivasan. "Fundamental Effects of Clustering on the Euclidean Embedding of Internet Hosts." In NETWORKING 2007. Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet, 890–901. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72606-7_76.

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Dai, Qionghai, and Yue Gao. "Neural Networks on Hypergraph." In Artificial Intelligence: Foundations, Theory, and Algorithms, 121–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_7.

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AbstractWith the development of deep learning on high-order correlations, hypergraph neural networks have received much attention in recent years. Generally, the neural networks on hypergraph can be divided into two categories, including the spectral-based methods and the spatial-based methods. For the spectral-based methods, the convolution operation is formulated in the spectral domain of graph, and we introduce the typical spectral-based methods, including hypergraph neural networks (HGNN), hypergraph convolution with attention (Hyper-Atten), and hyperbolic hypergraph neural network (HHGNN), which extend hypergraph computation to hyperbolic spaces beyond the Euclidean space. For the spatial-based methods, the convolution operation is defined in groups of spatially close vertices. We then present spatial-based hypergraph neural networks of the general hypergraph neural networks (HGNN+) and the dynamic hypergraph neural networks (DHGNN). Additionally, there are several convolution methods that attempt to reduce the hypergraph structure to the graph structure, so that the existing graph convolution methods can be directly deployed. Lastly, we analyze the association and comparison between hypergraph and graph in the two areas described above (spectral-based, spatial-based), further demonstrating the ability and advantages of hypergraph on constructing and computing higher-order correlations in the data.
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Young, Jonathan, Du Lei, and Andrea Mechelli. "Discriminative Log-Euclidean Kernels for Learning on Brain Networks." In Connectomics in NeuroImaging, 25–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67159-8_4.

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Rokka Chhetri, Sujit, and Mohammad Abdullah Al Faruque. "Non-euclidean Data-Driven Modeling Using Graph Convolutional Neural Networks." In Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis, 185–207. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37962-9_9.

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Heindl, Christoph. "py-microdots: Position Encoding in the Euclidean Plane Based on the Anoto Codec." In Lecture Notes in Networks and Systems, 219–35. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37963-5_16.

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Popa, Cosmin Radu. "A New FGMOST Euclidean Distance Computational Circuit Based on Algebraic Mean of the Input Potentials." In Artificial Neural Networks – ICANN 2009, 459–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04274-4_48.

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Purohit, G. N., Seema Verma, and Usha Sharma. "Application of Euclidean Distance Power Graphs in Localization of Sensor Networks." In Communications in Computer and Information Science, 367–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17881-8_35.

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Demiryurek, Ugur, Farnoush Banaei-Kashani, and Cyrus Shahabi. "Efficient Continuous Nearest Neighbor Query in Spatial Networks Using Euclidean Restriction." In Advances in Spatial and Temporal Databases, 25–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02982-0_5.

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Rodríguez, Sara Inés Rizo, and Francisco de Assis Tenorio de Carvalho. "Fuzzy Clustering Algorithm Based on Adaptive Euclidean Distance and Entropy Regularization for Interval-Valued Data." In Artificial Neural Networks and Machine Learning – ICANN 2018, 695–705. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01418-6_68.

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Conference papers on the topic "Euclidean networks"

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Huang, Shao-Lun, Changho Suh, and Lizhong Zheng. "Euclidean information theory of networks." In 2013 IEEE International Symposium on Information Theory (ISIT). IEEE, 2013. http://dx.doi.org/10.1109/isit.2013.6620335.

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Zhu, Hongbing, Chengdong Pu, Kei Eguchi, and Jinguang Gu. "Euclidean Particle Swarm Optimization." In 2009 Second International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2009. http://dx.doi.org/10.1109/icinis.2009.171.

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Li, Wentao, Min Gao, Wenge Rong, Junhao Wen, Qingyu Xiong, Ruixi Jia, and Tong Dou. "Social recommendation using Euclidean embedding." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965906.

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Celińska-Kopczyńska, Dorota, and Eryk Kopczyński. "Non-Euclidean Self-Organizing Maps." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/269.

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Self-Organizing Maps (SOMs, Kohonen networks) belong to neural network models of the unsupervised class. In this paper, we present the generalized setup for non-Euclidean SOMs. Most data analysts take it for granted to use some subregions of a flat space as their data model; however, by the assumption that the underlying geometry is non-Euclidean we obtain a new degree of freedom for the techniques that translate the similarities into spatial neighborhood relationships. We improve the traditional SOM algorithm by introducing topology-related extensions. Our proposition can be successfully applied to dimension reduction, clustering or finding similarities in big data (both hierarchical and non-hierarchical).
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Yin, Xunrui, Yan Wang, Xin Wang, Xiangyang Xue, and Zongpeng Li. "Min-cost multicast networks in Euclidean space." In 2012 IEEE International Symposium on Information Theory - ISIT. IEEE, 2012. http://dx.doi.org/10.1109/isit.2012.6283071.

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Afrasiyabi, Arman, Diaa Badawi, Baris Nasir, Ozan Yildi, Fatios T. Yarman Vural, and A. Enis Cetin. "Non-Euclidean Vector Product for Neural Networks." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461709.

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Costrell, Sarah, Subhrajit Bhattacharya, and Robert Ghrist. "Reconstruction of Euclidean embeddings in dense networks." In 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2016. http://dx.doi.org/10.1109/globalsip.2016.7905872.

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Davydov, Alexander, Anton V. Proskurnikov, and Francesco Bullo. "Non-Euclidean Contractivity of Recurrent Neural Networks." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867357.

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Malik, Muhammad Ammar, and Moonsoo Kang. "Euclidean distance based label noise cleaning." In 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2017. http://dx.doi.org/10.1109/icufn.2017.7993783.

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Khatir, Mehrdad, Nurendra Choudhary, Sutanay Choudhury, Khushbu Agarwal, and Chandan K. Reddy. "A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/431.

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Hyperbolic neural networks can effectively capture the inherent hierarchy of graph datasets, and consequently a powerful choice of GNNs. However, they entangle multiple incongruent (gyro-)vector spaces within a layer, which makes them limited in terms of generalization and scalability. In this work, we propose the Poincaré disk model as our search space, and apply all approximations on the disk (as if the disk is a tangent space derived from the origin), thus getting rid of all inter-space transformations. Such an approach enables us to propose a hyperbolic normalization layer and to further simplify the entire hyperbolic model to a Euclidean model cascaded with our hyperbolic normalization layer. We applied our proposed nonlinear hyperbolic normalization to the current state-of-the-art homogeneous and multi-relational graph networks. We demonstrate that our model not only leverages the power of Euclidean networks such as interpretability and efficient execution of various model components, but also outperforms both Euclidean and hyperbolic counterparts on various benchmarks. Our code is made publicly available at https://github.com/oom-debugger/ijcai23.
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