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51. Normal Analytic Compactifications of

Author

Mondal, Pinaki, Cheltsov, Ivan, editor, Ciliberto, Ciro, editor, Flenner, Hubert, editor, McKernan, James, editor, Prokhorov, Yuri G., editor, and Zaidenberg, Mikhail, editor

Published
2014
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53. Picking Planar Edges; or, Drawing a Graph with a Planar Subgraph

Author

Schaefer, Marcus, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Duncan, Christian, editor, and Symvonis, Antonios, editor

Published
2014
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54. Lower Bounds for Structuring Unreliable Radio Networks

Author

Newport, Calvin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Kuhn, Fabian, editor

Published
2014
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55. Improving Relational Consistency Algorithms Using Dynamic Relation Partitioning

Author

Schneider, Anthony, Woodward, Robert J., Choueiry, Berthe Y., Bessiere, Christian, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and O’Sullivan, Barry, editor

Published
2014
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56. Positive and Negative Proofs for Circuits and Branching Programs

Author

Dorzweiler, Olga, Flamm, Thomas, Krebs, Andreas, Ludwig, Michael, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Jürgensen, Helmut, editor, Karhumäki, Juhani, editor, and Okhotin, Alexander, editor

Published
2014
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57. A Methodological Approach to Integrate Ontology and Configurational Analysis

Author

Cataldo, Antonia, Di Pinto, Valerio, Rinaldi, Antonio M., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Rocha, Ana Maria A. C., editor, Torre, Carmelo, editor, Rocha, Jorge Gustavo, editor, Falcão, Maria Irene, editor, Taniar, David, editor, Apduhan, Bernady O., editor, and Gervasi, Osvaldo, editor

Published
2014
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58. Independence

Author

Klenke, Achim, Axler, Sheldon, Series editor, Capasso, Vincenzo, Series editor, Casacuberta, Carles, Series editor, MacIntyre, Angus, Series editor, Ribet, Kenneth, Series editor, Sabbah, Claude, Series editor, Süli, Endre, Series editor, Woyczynski, Wojbor A., Series editor, and Klenke, Achim

Published
2014
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62. Lozenge tilings of doubly-intruded hexagons.

Author

Ciucu, Mihai and Lai, Tri

Subjects
*HEXAGONS, *STATISTICAL mechanics, *TILES
Abstract

Motivated in part by Propp's intruded Aztec diamond regions, we consider hexagonal regions out of which two horizontal chains of triangular holes (called ferns) are removed, so that the chains are at the same height, and are attached to the boundary. By contrast with the intruded Aztec diamonds (whose numbers of domino tilings contain some large prime factors in their factorization), the number of lozenge tilings of our doubly-intruded hexagons turns out to be given by simple product formulas in which all factors are linear in the parameters. We present in fact q -versions of these formulas, which enumerate the corresponding plane-partitions-like structures by their volume. We also pose some natural statistical mechanics questions suggested by our set-up, which should be possible to tackle using our formulas. [ABSTRACT FROM AUTHOR]

Published
2019
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63. Graph-dual Laplacian principal component analysis.

Author

He, Jinrong, Bi, Yingzhou, Liu, Bin, and Zeng, Zhigao

Abstract

Principal component analysis is the most widely used method for linear dimensionality reduction, due to its effectiveness in exploring low-dimensional global geometric structures embedded in data. To preserve the intrinsic local geometrical structures of data, graph-Laplacian PCA (gLPCA) incorporates Laplacian embedding into PCA framework for learning local similarities between data points, which leads to significant performance improvement in clustering and classification. Some recent works showed that not only the high dimensional data reside on a low-dimensional manifold in the data space, but also the features lie on a manifold in feature space. However, both PCA and gLPCA overlook the local geometric information contained in the feature space. By considering the duality between data manifold and feature manifold, graph-dual Laplacian PCA (gDLPCA) is proposed, which incorporates data graph regularization and feature graph regularization into PCA framework to exploit local geometric structures of data manifold and feature manifold simultaneously. The experimental results on four benchmark data sets have confirmed its effectiveness and suggested that gDLPCA outperformed gLPCA on classification and clustering tasks. [ABSTRACT FROM AUTHOR]

Published
2019
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64. TOPOLOGY OF SPACES OF VALUATIONS AND GEOMETRY OF SINGULARITIES.

Author

DE FELIPE, ANA BELÉN

Subjects
*MATHEMATICAL singularities, *ALGEBRAIC varieties, *HOMEOMORPHISMS, *GEOMETRY, *ALGEBRAIC curves
Abstract

Given an algebraic variety X defined over an algebraically closed field, we study the space RZ(X, x) consisting of all the valuations of the function field of X which are centered in a closed point x of X. We concentrate on its homeomorphism type. We prove that, when x is a regular point, this homeomorphism type only depends on the dimension of X. If x is a singular point of a normal surface, we show that it only depends on the dual graph of a good resolution of (X, x) up to some precise equivalence. This is done by studying the relation between RZ(X, x) and the normalized non-Archimedean link of x in X coming from the point of view of Berkovich geometry. We prove that their behavior is the same. [ABSTRACT FROM AUTHOR]

Published
2019
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65. Enumeration of lozenge tilings of a hexagon with a shamrock missing on the symmetry axis.

Author

Lai, Tri and Rohatgi, Ranjan

Subjects
*RHOMBUSES (Shape), *HEXAGONS, *MATHEMATICAL symmetry, *PARTITIONS (Mathematics), *NUMBER theory
Abstract

Abstract In their paper about a dual of MacMahon's classical theorem on plane partitions, Ciucu and Krattenthaler proved a closed form product formula for the tiling number of a hexagon with a "shamrock", a union of four adjacent triangles, removed in the center (Ciucu and Krattenthaler, 2013). Lai later presented a q -enumeration for lozenge tilings of a hexagon with a shamrock removed from the boundary (Lai, 2017). It appears that the above are the only two positions of the shamrock hole that yield nice tiling enumerations. In this paper, we show that in the case of symmetric hexagons, we always have a simple product formula for the number of tilings when removing a shamrock at any position along the symmetry axis. Our result also generalizes Eisenkölbl's related work about lozenge tilings of a hexagon with two unit triangles missing on the symmetry axis (Eisenkölbl, 1999). [ABSTRACT FROM AUTHOR]

Published
2019
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66. Proof of a conjecture of Kenyon and Wilson on semicontiguous minors.

Author

Lai, Tri

Subjects
*MATRICES (Mathematics), *LAURENT series, *MATHEMATICAL proofs, *LATTICE field theory, *FUZZY topology
Abstract

Abstract Kenyon and Wilson showed how to test if a circular planar electrical network with n nodes is well-connected by checking the positivity of ( n 2 ) central minors of the response matrix. Their test is based on the fact that any contiguous minor of a matrix can be expressed as a Laurent polynomial in the central minors. Moreover, the Laurent polynomial is the generating function of domino tilings of a weighted Aztec diamond. They conjectured that a larger family of minors, semicontiguous minors, can also be written in terms of domino tilings of a region on the square lattice. In this paper, we present a proof of the conjecture. [ABSTRACT FROM AUTHOR]

Published
2019
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67. Dual graph wavelet neural network for graph-based semi-supervised classification

Author

Yinyin Chao, Yaqian Zhao, Yuan Ge, Li Rengang, Liu Haiwei, Hu Kekun, Dong Gang, and Jiang Dongdong

Subjects
Statistics and Probability, ComputingMethodologies_PATTERNRECOGNITION, Wavelet neural network, Artificial Intelligence, Dual graph, Computer science, business.industry, Graph based, General Engineering, Pattern recognition, Artificial intelligence, business
Abstract

Vertex classification is an important graph mining technique and has important applications in fields such as social recommendation and e-Commerce recommendation. Existing classification methods fail to make full use of the graph topology to improve the classification performance. To alleviate it, we propose a Dual Graph Wavelet neural Network composed of two identical graph wavelet neural networks sharing network parameters. These two networks are integrated with a semi-supervised loss function and carry out supervised learning and unsupervised learning on two matrixes representing the graph topology extracted from the same graph dataset, respectively. One matrix embeds the local consistency information and the other the global consistency information. To reduce the computational complexity of the convolution operation of the graph wavelet neural network, we design an approximate scheme based on the first type Chebyshev polynomial. Experimental results show that the proposed network significantly outperforms the state-of-the-art approaches for vertex classification on all three benchmark datasets and the proposed approximation scheme is validated for datasets with low vertex average degree when the approximation order is small.

Published
2022

68. Joint Adaptive Dual Graph and Feature Selection for Domain Adaptation

Author

Wei Wang, Mingshi Yan, Zhengming Ding, Fuming Sun, Jing Sun, Haojie Li, and Zhi-Hui Wang

Subjects
Contextual image classification, business.industry, Computer science, Feature vector, Feature selection, Pattern recognition, Domain (software engineering), Non-negative matrix factorization, Dual graph, Media Technology, Artificial intelligence, Electrical and Electronic Engineering, business, Subspace topology, Curse of dimensionality
Abstract

Domain adaptation aims to exploit domain-invariant features by aligning the cross-domain distributions in the manifold subspace for applying the classifier trained on the source domain to the target domain. However, two limitations may still deteriorate their performances: (1) the influences of noisy or irrelevant features in the original feature space are ignored, which may unexpectedly hurt the classification of target samples; (2) the graph constructed directly in the original data space cannot accurately capture the inherent local manifold structures of high-dimensional data due to the curse of dimensionality, which may seriously mislead the transferable features learning. In this paper, we propose a novel approach to address these problems, referred to as joint Adaptive Dual Graph and Feature Selection for domain adaptation (ADGFS). Specifically, feature selection can characterize the relative importance of different features through a scaling factor, which enables ADGFS to not only reduce the impacts of noisy or irrelevant features on knowledge transfer but also learn informative domain-invariant features. Meanwhile, ADGFS adaptively optimizes the dual graph by learning the similarity matrices of both instance-level and feature-level graphs in the projected low-dimensional manifold subspace rather than the original high-dimensional space, such that the intrinsic local manifold structures of data can be captured precisely. Moreover, ADGFS simultaneously aligns the marginal and conditional probability distributions in the nonnegative matrix factorization framework to narrow the distribution discrepancies between the two different domains, which can adequately transfer knowledge from the source domain to the target domain. Comprehensive experiments on four benchmark datasets can demonstrate that the effectiveness of the proposed approach in cross-domain image classification.

Published
2022

69. K-Connected Cores Computation in Large Dual Networks.

Author

Cui, Lizhen, Yue, Lingxi, Wen, Dong, and Qin, Lu

Subjects
GRAPH theory, VERTEX operator algebras, SOCIAL networks, SEARCH algorithms, QUERYING (Computer science)
Abstract

Computing k-cores is a fundamental and important graph problem, which can be applied in many areas, such as community detection, network visualization, and network topology analysis. Due to the complex relationship between different entities, dual graph widely exists in the applications. A dual graph contains a physical graph and a conceptual graph, both of which have the same vertex set. Given that there exist no previous studies on the k-core in dual graphs, we formulate a k-connected core (k-CCO) model in dual graphs. A k-CCO is a k-core in the conceptual graph, and also connected in the physical graph. Given a dual graph and an integer k, we propose a polynomial time algorithm for computing all k-CCOs. We also propose three algorithms for computing all maximum-connected cores (MCCO), which are the existing k-CCOs such that a (k+1)-CCO does not exist. We further study a subgraph search problem, which is computing a k-CCO that contains a set of query vertices. We propose an index-based approach to efficiently answer the query for any given parameter k. We conduct extensive experiments on six real-world datasets and four synthetic datasets. The experimental results demonstrate the effectiveness and efficiency of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Published
2018
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70. Counting spanning trees with a Kekulé structure in linear hexagonal chains.

Author

Li, Danyi and Yan, Weigen

Subjects
*SPANNING trees, *GRAPH theory, *MOLECULAR graphs, *MOLECULAR connectivity index, *COUNTING
Abstract

• We enumerate spanning trees with a Kekule structure in the linear hexagonal chains on the plane. • We enumerate spanning trees with a Kekule structure in the linear hexagonal chains on the cylinder. • We enumerate spanning trees with a Kekule structure in the linear hexagonal chains on the Mobius strip. In chemical graph theory, many topological indices of the hexagonal chains, for instance, the energy, Wiener and Kirchhoff indices, and numbers of Kekulé structures and spanning trees, and so on, have been studied extensively. We enumerate spanning trees with a Kekulé structure in the linear hexagonal chains on the plane, cylinder and Möbius strip, respectively. [ABSTRACT FROM AUTHOR]

Published
2023
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71. Auto weighted robust dual graph nonnegative matrix factorization for multiview clustering.

Author

Jia, Mengxue, Liu, Sanyang, and Bai, Yiguang

Subjects
MATRIX decomposition, NONNEGATIVE matrices, FACTORIZATION, MACHINE learning
Abstract

Recent multi-data fusion techniques have inspired multi-view clustering study, which has become a popular topic in machine learning. One successful way of solving multi-view clustering is nonnegative matrix factorization. This paper proposes an auto weighted robust dual graph nonnegative matrix factorization (ARDNMF), where ℓ 2 , 1 norm is adopted to make ARDNMF robust as data contain noise and outliers. Through dual graphs, structure information in data space and feature space is considered. Moreover, an auto weighted assignment method is introduced in ARDNMF to assign appropriate weights to views. Effective updating rules are given. Experiments on real datasets show ARDNMF superiors to state-of-the-art algorithms in terms of clustering results. • The application of the ℓ 2 , 1 norm makes ARDNMF robust to noise and outliers. • The structure information of each view data is utilized sufficiently to avoid information loss. • An auto weighted strategy is employed, which makes ARDNMF find complementary information among different views flexibly. [ABSTRACT FROM AUTHOR]

Published
2023
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72. Dual Graph Convolutional Network for Hyperspectral Image Classification With Limited Training Samples

Author

Pedram Ghamisi, Xin He, and Yushi Chen

Subjects
business.industry, Computer science, Feature extraction, Hyperspectral imaging, Pattern recognition, Overfitting, Convolutional neural network, Regularization (mathematics), Dual graph, Feature (computer vision), General Earth and Planetary Sciences, Artificial intelligence, Electrical and Electronic Engineering, business, Dropout (neural networks)
Abstract

Due to powerful feature extraction capability, convolutional neural networks (CNNs) have been widely used for hyperspectral image (HSI) classification. However, because of a large number of parameters that need to be trained, sufficient training samples are usually required for deep CNN-based methods. Unfortunately, limited training samples are a common issue in the remote sensing community. In this study, a dual graph convolutional network (DGCN) is proposed for the supervised classification of HSI with limited training samples. The first GCN fully extracts features existing in and among HSI samples, while the second GCN utilizes label distribution learning, and thus, it potentially reduces the number of required training samples. The two GCNs are integrated through several iterations to decrease interclass distances, which leads to a more accurate classification step. Moreover, a new idea entitled multiscale feature cutout is proposed as a regularization technique for HSI classification (DGCN-M). Different from the regularization methods (e.g., dropout and DropBlock), the proposed multiscale feature cutout could randomly mask out multiscale region sizes in a feature map, which further reduces the overfitting problem and yields consistent improvement. Experimental results on the four popular hyperspectral data sets (i.e., Salinas, Indian Pines, Pavia, and Houston) indicate that the proposed method obtains good classification performance compared to state-of-the-art methods, which shows the potential of GCN for HSI classification.

Published
2022

73. Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks

Author

Donghong Gu, Hua Wang, Yun Xue, Xiaohui Hu, Luwei Xiao, and Yongsheng Zhu

Subjects
Dependency (UML), Artificial neural network, Computer science, business.industry, Cognitive Neuroscience, Deep learning, Machine learning, computer.software_genre, Convolutional neural network, Computer Science Applications, Dependency graph, Recurrent neural network, Artificial Intelligence, Dual graph, Artificial intelligence, business, computer, Connectivity
Abstract

The goal of aspect-based sentiment classification (ASC) is to predict the corresponding emotion of a specific target of a sentence. In neural network-based methods for ASC, various sophisticated models such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) are widespread. Recently, ongoing research has integrated syntactic structures into graph neural networks (GNN) to deal with ASC tasks. However, these methods are limited due to the noise and inefficient use of information of syntactic dependency trees. This paper proposes a novel GNN based deep learning model to overcome the deficiencies of prior studies. In the proposed model, to exploit the information in the syntactic dependency trees, a novel part-of-speech (POS) guided syntactic dependency graph is constructed for a relational graph attention network (RGAT) to eliminate the noises. Further, a syntactic distance attention-guided layer is designed for a densely connected graph convolutional network (DCGCN), which can fully extract semantic dependency between contextual words. Experiments on three public datasets are carried out to evaluate the effectiveness of the proposed model. Comparing to the baselines, our model, as a best alternative, achieves state-of-arts performance.

Published
2022

74. Dynamic subspace dual-graph regularized multi-label feature selection

Author

Yonghao Li, Juncheng Hu, Wanfu Gao, and Gaochao Xu

Subjects
business.industry, Computer science, Cognitive Neuroscience, Process (computing), Pattern recognition, Feature selection, Space (commercial competition), Computer Science Applications, Matrix (mathematics), ComputingMethodologies_PATTERNRECOGNITION, Artificial Intelligence, Dual graph, Graph (abstract data type), Artificial intelligence, Laplacian matrix, business, Subspace topology
Abstract

In multi-label learning, feature selection is a topical issue for addressing high-dimension data. However, most of existing methods adopt imperfect labels to perform feature selection. Although some graph-based multi-label feature selection methods are proposed to deal with the problem, they adopt the fixed graph Laplacian matrix so that the performances of these models are under-performing. To this end, this paper proposes a Dynamic Subspace dual-graph regularized Multi-label Feature Selection method named DSMFS. DSMFS decomposes the original label space into a low-dimensional subspace, and then both the dynamic label-level subspace graph and the feature-level graph are used to obtain a high-quality label subspace to conduct feature selection process. Seven state-of-the-art methods are compared to the proposed method on twelve multi-label benchmark data sets in the experiments. Experimental results demonstrate the superiority of DSMFS.

Published
2022

75. Maps

Author

Pisanski, Tomaž, Servatius, Brigitte, Pisanski, Tomaž, and Servatius, Brigitte

Published
2013
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78. Rolling Upward Planarity Testing of Strongly Connected Graphs

Author

Auer, Christopher, Bachmaier, Christian, Brandenburg, Franz J., Hanauer, Kathrin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Brandstädt, Andreas, editor, Jansen, Klaus, editor, and Reischuk, Rüdiger, editor

Published
2013
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79. Space-Efficient and Data-Sensitive Polygon Reconstruction Algorithms from Visibility Angle Information

Author

Chun, Jinhee, de Gonzalo, Ricardo Garcia, Tokuyama, Takeshi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cai, Leizhen, editor, Cheng, Siu-Wing, editor, and Lam, Tak-Wah, editor

Published
2013
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80. Guarding Thin Orthogonal Polygons Is Hard

Author

Tomás, Ana Paula, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gąsieniec, Leszek, editor, and Wolter, Frank, editor

Published
2013
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81. A Duality Transform for Constructing Small Grid Embeddings of 3D Polytopes

Author

Igamberdiev, Alexander, Schulz, André, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wismath, Stephen, editor, and Wolff, Alexander, editor

Published
2013
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83. The Beginning of the World

Author

Hinz, Andreas M., Klavžar, Sandi, Milutinović, Uroš, Petr, Ciril, Hinz, Andreas M., Klavžar, Sandi, Milutinović, Uroš, and Petr, Ciril

Published
2013
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86. The Duals of Upward Planar Graphs on Cylinders

Author

Auer, Christopher, Bachmaier, Christian, Brandenburg, Franz J., Gleißner, Andreas, Hanauer, Kathrin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Golumbic, Martin Charles, editor, Stern, Michal, editor, Levy, Avivit, editor, and Morgenstern, Gila, editor

Published
2012
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87. Bounds on Contention Management in Radio Networks

Author

Ghaffari, Mohsen, Haeupler, Bernhard, Lynch, Nancy, Newport, Calvin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Aguilera, Marcos K., editor

Published
2012
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88. Revisiting Neighborhood Inverse Consistency on Binary CSPs

Author

Woodward, Robert J., Karakashian, Shant, Choueiry, Berthe Y., Bessiere, Christian, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Milano, Michela, editor

Published
2012
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89. Interactive Control of Mesh Topology in Quadrilateral Mesh Generation Based on 2D Tensor Fields

Author

Bi, Chongke, Sakurai, Daisuke, Takahashi, Shigeo, Ono, Kenji, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Fowlkes, Charless, editor, Wang, Sen, editor, Choi, Min-Hyung, editor, Mantler, Stephan, editor, Schulze, Jürgen, editor, Acevedo, Daniel, editor, Mueller, Klaus, editor, and Papka, Michael, editor

Published
2012
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90. Planar Open Rectangle-of-Influence Drawings with Non-aligned Frames

Author

Alamdari, Soroush, Biedl, Therese, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, van Kreveld, Marc, editor, and Speckmann, Bettina, editor

Published
2012
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91. Curvature Minimization for Surface Reconstruction with Features

Author

Shi, Juan, Wan, Min, Tai, Xue-Cheng, Wang, Desheng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bruckstein, Alfred M., editor, ter Haar Romeny, Bart M., editor, Bronstein, Alexander M., editor, and Bronstein, Michael M., editor

Published
2012
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92. DACHA: A Dual Graph Convolution Based Temporal Knowledge Graph Representation Learning Method Using Historical Relation

Author

TangXing, ZhangYongjun, LiYansheng, ChenLing, ChenWeiqi, and QianYuntao

Subjects
Theoretical computer science, General Computer Science, Relation (database), Knowledge graph, Dual graph, Computer science, Temporal information, Feature learning, Convolution, Vector space
Abstract

Temporal knowledge graph (TKG) representation learning embeds relations and entities into a continuous low-dimensional vector space by incorporating temporal information. Latest studies mainly aim at learning entity representations by modeling entity interactions from the neighbor structure of the graph. However, the interactions of relations from the neighbor structure of the graph are neglected, which are also of significance for learning informative representations. In addition, there still lacks an effective historical relation encoder to model the multi-range temporal dependencies. In this article, we propose a d ual gr a ph c onvolution network based TKG representation learning method using h istorical rel a tions (DACHA). Specifically, we first construct the primal graph according to historical relations, as well as the edge graph by regarding historical relations as nodes. Then, we employ the dual graph convolution network to capture the interactions of both entities and historical relations from the neighbor structure of the graph. In addition, the temporal self-attentive historical relation encoder is proposed to explicitly model both local and global temporal dependencies. Extensive experiments on two event based TKG datasets demonstrate that DACHA achieves the state-of-the-art results.

Published
2021

93. Correntropy-based dual graph regularized nonnegative matrix factorization with Lp smoothness for data representation

Author

Zhengtao Yu, Zhen Liu, Congzhe You, Weng Zonghui, Xiaojun Wu, Zhenqiu Shu, and Songze Tang

Subjects
Euclidean distance, Smoothness (probability theory), Artificial Intelligence, Computer science, Dual graph, Feature vector, Lp space, Cluster analysis, Algorithm, Measure (mathematics), Non-negative matrix factorization
Abstract

Nonnegative matrix factorization methods have been widely used in many applications in recent years. However, the clustering performances of such methods may deteriorate dramatically in the presence of non-Gaussian noise or outliers. To overcome this problem, in this paper, we propose correntropy-based dual graph regularized NMF with LP smoothness (CDNMFS) for data representation. Specifically, we employ correntropy instead of the Euclidean norm to measure the incurred reconstruction error. Furthermore, we explore the geometric structures of both the input data and the feature space and impose an Lp norm constraint to obtain an accurate solution. In addition, we introduce an efficient optimization scheme for the proposed model and present its convergence analysis. Experimental results on several image datasets demonstrate the superiority of the proposed CDNMFS method.

Published
2021

96. Restricted Cuts for Bisections in Solid Grids: A Proof via Polygons

Author

Feldmann, Andreas Emil, Das, Shantanu, Widmayer, Peter, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Kolman, Petr, editor, and Kratochvíl, Jan, editor

Published
2011
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97. Common Unfoldings of Polyominoes and Polycubes

Author

Aloupis, Greg, Bose, Prosenjit K., Collette, Sébastien, Demaine, Erik D., Demaine, Martin L., Douïeb, Karim, Dujmović, Vida, Iacono, John, Langerman, Stefan, Morin, Pat, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Akiyama, Jin, editor, Bo, Jiang, editor, Kano, Mikio, editor, and Tan, Xuehou, editor

Published
2011
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98. Shortest Path Based Planar Graph Cuts for Bi-layer Segmentation of Binocular Stereo Video

Author

Huang, Xiangsheng, Gong, Lujin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Koch, Reinhard, editor, and Huang, Fay, editor

Published
2011
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99. Kinetic Red-Blue Minimum Separating Circle

Author

Cheung, Yam Ki, Daescu, Ovidiu, Zivanic, Marko, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Wang, Weifan, editor, Zhu, Xuding, editor, and Du, Ding-Zhu, editor

Published
2011
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100. Adjacency-Preserving Spatial Treemaps

Author

Buchin, Kevin, Eppstein, David, Löffler, Maarten, Nöllenburg, Martin, Silveira, Rodrigo I., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Dehne, Frank, editor, Iacono, John, editor, and Sack, Jörg-Rüdiger, editor

Published
2011
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