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"abstract": "In recent times, Generative Adversarial Networks (GANs) have created a lot of buzz in the research community. GANs are formulated on the zero-sum game theory, where two neural nets compete against each other. The resultant deep model is capable of generating data similar to any data distribution provided. It utilizes the adversarial learning approach and is far more capable in learning features than the traditional machine learning models. This review focusses on the origin and evolution of GANs. Firstly, the traditional GAN is explored in terms of its structure and loss functions. Then come the common challenges of training GANs. Thirdly, the review dives into numerous GAN variants and explains their improvements. The review then lists the wide variety of applications and ends with the conclusion.",
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"abstract": "Current VR (Virtual Reality) browsing interfaces provide few landmarks, particularly, less is known about the effects of 3D landmarks on facilitating spatial learning and recalling for VR browsing interfaces. In this work, the implementation of a new artificial landmark (3D visual pin) along with three previous landmarks (grid, image, and anchor) are presented with a curved visual wall layout, in which we examined the effects on facilitating learning and recalling items' locations in small and large item sets. We found that both the 3D pin and the anchor interfaces assisted multiple targets retrieving and recalling, compared to the grid interface, and more preferences of 3D pins, but no significant difference of retrieval and recall performances between the 3D pin and the anchor interfaces. The image interface had little impact on trial completion time, but significantly improved the selection accuracy compared to the grid interface. The findings provide the possibility of enabling artificial landmarks for future VR browsing interfaces.",
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"abstract": "The importance of triangle meshes with a special kind of connectivity, the so-called subdivision connectivity is still growing. Therefore it is important to develop efficient algorithms for converting a given mesh with arbitrary connectivity into one with subdivision connectivity. We focus on 2-manifold triangle meshes with a boundary and no holes. We discuss the importance of a parametrization with minimal distortion for the process of remeshing. Based on the concept of most isometric parameterizations we have developed a remeshing algorithm for the given class of triangle meshes. A series of examples shows the advantages of our approach.",
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"abstract": "Quality surface meshes for molecular models are desirable in the studies of protein shapes and functionalities. However, there is still no robust software that is capable to generate such meshes with good quality. In this paper, we present a Delaunay-based surface triangulation algorithm generating quality surface meshes for the molecular skin model. We expand the restricted union of balls along the surface and generate an /spl epsiv/-sampling of the skin surface incrementally. At the same time, a quality surface mesh is extracted from the Delaunay triangulation of the sample points. The algorithm supports robust and efficient implementation and guarantees the mesh quality and topology as well. Our results facilitate molecular visualization and have made a contribution towards generating quality volumetric tetrahedral meshes for the macromolecules.",
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"abstract": "We propose a 3D image display system which can present real scenes with realistic motion parallax. In the sensing system, a scene is observed by using a camera matrix. An excellent stereo algorithm SEA which utilizes 3x3 image matrix recovers the depth information of the scene with the density and the sharpness required for high quality image generation. In the display system, following the viewing position of the observer, 2D images with proper motion parallax are generated and presented. A novel algorithm to determine the view parameters suitable for reproducing the motion parallax on a fixed screen and an image generation algorithm which can cope with arbitrary viewing positions are described. A prototype system has been developed to demonstrate the feasibility of the proposed algorithms.",
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"abstract": "This paper studies the Tensor Robust Principal Component (TRPCA) problem which extends the known Robust PCA [4] to the tensor case. Our model is based on a new tensor Singular Value Decomposition (t-SVD) [14] and its induced tensor tubal rank and tensor nuclear norm. Consider that we have a 3-way tensor X ε Rn1×n2×n3 such that X = L0 + S0, where L0 has low tubal rank and S0 is sparse. Is that possible to recover both components? In this work, we prove that under certain suitable assumptions, we can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the l1-norm, i.e., min L, E ||L||* + λ||ε||1, s.t. X = L + ε, where λ = 1/√max(n1, n2)n3. Interestingly, TRPCA involves RPCA as a special case when n3 = 1 and thus it is a simple and elegant tensor extension of RPCA. Also numerical experiments verify our theory and the application for the image denoising demonstrates the effectiveness of our method.",
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"abstract": "Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance. However, the resultant optimization problem is much more challenging. A very recent state-of-the-art is based on the proximal gradient algorithm. However, it requires an expensive full SVD in each proximal step. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, a cutoff can be derived to automatically threshold the singular values obtained from the proximal operator. This allows the use of power method to approximate the SVD efficiently. Besides, the proximal operator can be reduced to that of a much smaller matrix projected onto this leading subspace. Convergence, with a rate of O(1/T) where T is the number of iterations, can be guaranteed. Extensive experiments are performed on matrix completion and robust principal component analysis. The proposed method achieves significant speedup over the state-of-the-art. Moreover, the matrix solution obtained is more accurate and has a lower rank than that of the traditional nuclear norm regularizer.",
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"title": "A Fast Algorithm for Weighted Tensor Rank Minimization",
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"abstract": "Recently, tensor rank minimization problem with the sum of nuclear norm(SNN) and its variants has received significant attention. Convex relaxations of tensor rank guarantee the global convergence. However, the sum of nuclear norm(SNN) is not a tight relaxation and the computation of full SVD is very expensive. In this paper, we extend the weighted matrix rank to tensor rank to balance the penalization of different singular values. We also use a randomized projection technique to accelerate the computation. The experiments confirm the benefits of our choice of regularization and the proposed algorithms outperform state-of-the-art algorithms on synthetic and real datasets.",
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"affiliation": "University of Science and Technology of China",
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"abstract": "Recently, Musco and Woodruff (FOCS, 2017) showed that given an n×n positive semidefinite (PSD) matrix A, it is possible to compute a (1+ε-approximate relative-error low-rank approximation to A by querying Õ(nk/ε<sup>2.5</sup>) entries of A in time Õ(nk/ε<sup>2.5</sup>+nk<sup>ω-1</sup>/ε<sup>2(ω-1)</sup>). They also showed that any relative-error low-rank approximation algorithm must query Ω(nk/ε) entries of A, this gap has since remained open. Our main result is to resolve this question by obtaining an optimal algorithm that queries Õ(nk/ε) entries of A and outputs a relative-error low-rank approximation in Õ(n·(k/ε)<sup>ω-1</sup>) time. Note, our running time improves that of Musco and Woodruff, and matches the information-theoretic lower bound if the matrix-multiplication exponent ω is 2. We then extend our techniques to negative-type distance matrices. Here, our input is a pair-wise distance matrix A corresponding to a point set P={x<sub>1</sub>, x<sub>2</sub>, ..., x<sub>n</sub>} such that A<sub>i,</sub><sub>j</sub>=||x<sub>i</sub>-x<sub>j</sub>||2<sup>2</sup>. Bakshi and Woodruff (NeurIPS, 2018) showed a bi-criteria, relative-error low-rank approximation for negative-type metrics. Their algorithm queries Õ(nk/ε<sup>2.5</sup>) entries and outputs a rank-( k+4) matrix. We show that the bi-criteria guarantee is not necessary and obtain an Õ(nk/ε) query algorithm, which is optimal. Our algorithm applies to all distance matrices that arise from metrics satisfying negative-type inequalities, including l<sub>1</sub>,l<sub>2</sub>, spherical metrics, hypermetrics and effective resistances on a graph. We also obtain faster algorithms for ridge regression. Next, we introduce a new robust low-rank approximation model which captures PSD matrices that have been corrupted with noise. We assume that the Frobenius norm of the corruption is bounded. Here, we relax the notion of approximation to additive-error, since it is information-theoretically impossible to obtain a relative-error approximation in this setting. While a sample complexity lower bound precludes sublinear algorithms for arbitrary PSD matrices, we provide the first sublinear time and query algorithms when the corruption on the diagonal entries is bounded. As a special case, we show sample-optimal sublinear time algorithms for low-rank approximation of correlation matrices corrupted by noise.",
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{
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"content": "Recently, Musco and Woodruff (FOCS, 2017) showed that given an n×n positive semidefinite (PSD) matrix A, it is possible to compute a (1+ε-approximate relative-error low-rank approximation to A by querying Õ(nk/ε<sup>2.5</sup>) entries of A in time Õ(nk/ε<sup>2.5</sup>+nk<sup>ω-1</sup>/ε<sup>2(ω-1)</sup>). They also showed that any relative-error low-rank approximation algorithm must query Ω(nk/ε) entries of A, this gap has since remained open. Our main result is to resolve this question by obtaining an optimal algorithm that queries Õ(nk/ε) entries of A and outputs a relative-error low-rank approximation in Õ(n·(k/ε)<sup>ω-1</sup>) time. Note, our running time improves that of Musco and Woodruff, and matches the information-theoretic lower bound if the matrix-multiplication exponent ω is 2. We then extend our techniques to negative-type distance matrices. Here, our input is a pair-wise distance matrix A corresponding to a point set P={x<sub>1</sub>, x<sub>2</sub>, ..., x<sub>n</sub>} such that A<sub>i,</sub><sub>j</sub>=||x<sub>i</sub>-x<sub>j</sub>||2<sup>2</sup>. Bakshi and Woodruff (NeurIPS, 2018) showed a bi-criteria, relative-error low-rank approximation for negative-type metrics. Their algorithm queries Õ(nk/ε<sup>2.5</sup>) entries and outputs a rank-( k+4) matrix. We show that the bi-criteria guarantee is not necessary and obtain an Õ(nk/ε) query algorithm, which is optimal. Our algorithm applies to all distance matrices that arise from metrics satisfying negative-type inequalities, including l<sub>1</sub>,l<sub>2</sub>, spherical metrics, hypermetrics and effective resistances on a graph. We also obtain faster algorithms for ridge regression. Next, we introduce a new robust low-rank approximation model which captures PSD matrices that have been corrupted with noise. We assume that the Frobenius norm of the corruption is bounded. Here, we relax the notion of approximation to additive-error, since it is information-theoretically impossible to obtain a relative-error approximation in this setting. While a sample complexity lower bound precludes sublinear algorithms for arbitrary PSD matrices, we provide the first sublinear time and query algorithms when the corruption on the diagonal entries is bounded. As a special case, we show sample-optimal sublinear time algorithms for low-rank approximation of correlation matrices corrupted by noise.",
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"normalizedAbstract": "Recently, Musco and Woodruff (FOCS, 2017) showed that given an n×n positive semidefinite (PSD) matrix A, it is possible to compute a (1+ε-approximate relative-error low-rank approximation to A by querying Õ(nk/ε2.5) entries of A in time Õ(nk/ε2.5+nkω-1/ε2(ω-1)). They also showed that any relative-error low-rank approximation algorithm must query Ω(nk/ε) entries of A, this gap has since remained open. Our main result is to resolve this question by obtaining an optimal algorithm that queries Õ(nk/ε) entries of A and outputs a relative-error low-rank approximation in Õ(n·(k/ε)ω-1) time. Note, our running time improves that of Musco and Woodruff, and matches the information-theoretic lower bound if the matrix-multiplication exponent ω is 2. We then extend our techniques to negative-type distance matrices. Here, our input is a pair-wise distance matrix A corresponding to a point set P={x1, x2, ..., xn} such that Ai,j=||xi-xj||22. Bakshi and Woodruff (NeurIPS, 2018) showed a bi-criteria, relative-error low-rank approximation for negative-type metrics. Their algorithm queries Õ(nk/ε2.5) entries and outputs a rank-( k+4) matrix. We show that the bi-criteria guarantee is not necessary and obtain an Õ(nk/ε) query algorithm, which is optimal. Our algorithm applies to all distance matrices that arise from metrics satisfying negative-type inequalities, including l1,l2, spherical metrics, hypermetrics and effective resistances on a graph. We also obtain faster algorithms for ridge regression. Next, we introduce a new robust low-rank approximation model which captures PSD matrices that have been corrupted with noise. We assume that the Frobenius norm of the corruption is bounded. Here, we relax the notion of approximation to additive-error, since it is information-theoretically impossible to obtain a relative-error approximation in this setting. While a sample complexity lower bound precludes sublinear algorithms for arbitrary PSD matrices, we provide the first sublinear time and query algorithms when the corruption on the diagonal entries is bounded. As a special case, we show sample-optimal sublinear time algorithms for low-rank approximation of correlation matrices corrupted by noise.",
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"abstract": "With the recent availability of commercial light field cameras, we can foresee a future in which light field signals will be as common place as images. Hence, there is an imminent need to address the problem of light field processing. We provide a common framework for addressing many of the light field processing tasks, such as denoising, angular and spatial superresolution, etc. (in essence, all processing tasks whose observation models are linear). We propose a patch based approach, where we model the light field patches using a Gaussian mixture model (GMM). We use the ”disparity pattern” of the light field data to design the patch prior. We show that the light field patches with the same disparity value (i.e., at the same depth from the focal plane) lie on a low-dimensional subspace and that the dimensionality of such subspaces varies quadratically with the disparity value. We then model the patches as Gaussian random variables conditioned on its disparity value, thus, effectively leading to a GMM model. During inference, we first find the disparity value of a patch by a fast subspace projection technique and then reconstruct it using the LMMSE algorithm. With this prior and inference algorithm, we show that we can perform many different processing tasks under a common framework.",
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"content": "With the recent availability of commercial light field cameras, we can foresee a future in which light field signals will be as common place as images. Hence, there is an imminent need to address the problem of light field processing. We provide a common framework for addressing many of the light field processing tasks, such as denoising, angular and spatial superresolution, etc. (in essence, all processing tasks whose observation models are linear). We propose a patch based approach, where we model the light field patches using a Gaussian mixture model (GMM). We use the ”disparity pattern” of the light field data to design the patch prior. We show that the light field patches with the same disparity value (i.e., at the same depth from the focal plane) lie on a low-dimensional subspace and that the dimensionality of such subspaces varies quadratically with the disparity value. We then model the patches as Gaussian random variables conditioned on its disparity value, thus, effectively leading to a GMM model. During inference, we first find the disparity value of a patch by a fast subspace projection technique and then reconstruct it using the LMMSE algorithm. With this prior and inference algorithm, we show that we can perform many different processing tasks under a common framework.",
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"title": "A Stereo-Vision-Assisted model for depth map super-resolution",
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"abstract": "In this paper, we propose a novel Stereo-Vision-Assisted (SVA) model for depth map super-resolution. Given a low-resolution depth map as input, we investigate to enhance its resolution or quality using the registered and potentially highresolution color stereo image pair. First, based on the mutual benefits between raw depth map and features of highresolution color image, we model the relationship with two constraint terms of local and non-local priors which sufficiently explore their complementary nature. Then by considering reliable disparity pixels calculated from stereo matching algorithm, we formulate a stereo disparity regularization term to further reinforce the preservation of fine depth detail. In addition, we employ an efficient iterative algorithm to optimize the objective function. Experimental results demonstrate that our approach can achieve high-quality depth map in terms of both spatial resolution and depth precision.",
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"affiliation": "Department of Electronics and Information, Hangzhou Dianzi University, Hangzhou, China",
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"affiliation": "Department of Computer Science, University of Texas at San Antonio, USA",
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"abstract": "The Tioga system applies a boxes and arrows programming notation to allow nonexpert users to graphically construct database applications. Users connect database procedures using a dataflow model. Browsers are used to visualize the resulting data. This paper describes extensions to the Tioga browser protocol. These extensions allow sophisticated, flight-simulator navigation through a multidimensional data space. This design also incorporates wormholes to allow tunneling between different multidimensional spaces. Wormholes are shown to be substantial generalizations of hyperlinks in a hypertext system. These powerful mechanisms for relating data provide users with great flexibility. For example, users can create magnifying glasses that provide an enhanced view of the underlying data.<>",
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"abstract": "The authors summarize experiments performed to establish prove card maintenance techniques and standards. These techniques are designed to improve the probe's contact quality when testing dice on wafers in production testing and are based on two concepts: probe contact resistance and probe mark quality. The results indicate that the best material to use is sanding paper in a circular movement with a minimum of pressure applied to the probes. This pressure should gradually increase during the sanding operation. Ceramic material is not recommended for probe maintenance because of the resulting poor probe marks. Contact resistance measurements should be performed using the same metallized surface used for production wafers. If it is necessary to exercise the probe card, it should be done before the sanding operation. The sanding should always be the last operation before solvent cleaning.<>",
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"content": "The authors summarize experiments performed to establish prove card maintenance techniques and standards. These techniques are designed to improve the probe's contact quality when testing dice on wafers in production testing and are based on two concepts: probe contact resistance and probe mark quality. The results indicate that the best material to use is sanding paper in a circular movement with a minimum of pressure applied to the probes. This pressure should gradually increase during the sanding operation. Ceramic material is not recommended for probe maintenance because of the resulting poor probe marks. Contact resistance measurements should be performed using the same metallized surface used for production wafers. If it is necessary to exercise the probe card, it should be done before the sanding operation. The sanding should always be the last operation before solvent cleaning.<>",
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"abstract": "Concerns for environmental sustainability are leading to a growing interest understanding geospatial data. At the same time, the availability of high-resolution imagery from satellites and unmanned air systems is increasing rapidly. However, the processing techniques that are available within geographic information systems are not yet adapted to big data challenges and opportunities. We propose a logarithmically scaling alternative to widely used window aggregation techniques, and integrate it with the localized computation of regression lines and correlations. Our approach, furthermore, enables a multi-scalar analysis at no additional computational cost. We demonstrate the potential for the proposed technique in the context of agriculturally relevant relationships, in particular between yield and vegetation indexes, which have the goal of reducing fertilizer use through a targeted application of nitrogen during the season. To demonstrate scaling and general usefulness of our techniques we furthermore examine relationships between different bands of remotely sensed data.",
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"content": "Concerns for environmental sustainability are leading to a growing interest understanding geospatial data. At the same time, the availability of high-resolution imagery from satellites and unmanned air systems is increasing rapidly. However, the processing techniques that are available within geographic information systems are not yet adapted to big data challenges and opportunities. We propose a logarithmically scaling alternative to widely used window aggregation techniques, and integrate it with the localized computation of regression lines and correlations. Our approach, furthermore, enables a multi-scalar analysis at no additional computational cost. We demonstrate the potential for the proposed technique in the context of agriculturally relevant relationships, in particular between yield and vegetation indexes, which have the goal of reducing fertilizer use through a targeted application of nitrogen during the season. To demonstrate scaling and general usefulness of our techniques we furthermore examine relationships between different bands of remotely sensed data.",
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"id": "proceedings/semapro/2009/3833/0/3833a113",
"title": "Personalized Access to Information by Query Reformulation Based on the State of the Current Task and User Profile",
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"title": "2009 20th International Workshop on Database and Expert Systems Application. DEXA 2009",
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"title": "Improving Web Page Retrieval Using Search Context from Clicked Domain Names",
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"abstract": "Search context is a crucial factor that helps to understand a user’s information need in ad-hoc Web page retrieval. A query log of a search engine contains rich information on issued queries and their corresponding clicked Web pages. The clicked data implies its relevance to the query and can be used to define the topical context. However, the log is usually not completely available due to privacy concerns. In this paper, we derive clicked pages from clicked domains and use the surrounding query context to enhance retrieval performance. One strategy is to promote clicked pages directly in the initial retrieval result. Another strategy is to expand the original query using selected terms from the clicked pages. Our experimental results on the TREC GOV2 data and a query log of a major search engine show that both strategies can boost retrieval performance compared to the standard language model and pseudo relevance feedback (PRF) model. Their good performance on early precision allows us to apply PRF further for even more accurate result that is comparable to the performance of true relevance feedback.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Search context is a crucial factor that helps to understand a user’s information need in ad-hoc Web page retrieval. A query log of a search engine contains rich information on issued queries and their corresponding clicked Web pages. The clicked data implies its relevance to the query and can be used to define the topical context. However, the log is usually not completely available due to privacy concerns. In this paper, we derive clicked pages from clicked domains and use the surrounding query context to enhance retrieval performance. One strategy is to promote clicked pages directly in the initial retrieval result. Another strategy is to expand the original query using selected terms from the clicked pages. Our experimental results on the TREC GOV2 data and a query log of a major search engine show that both strategies can boost retrieval performance compared to the standard language model and pseudo relevance feedback (PRF) model. Their good performance on early precision allows us to apply PRF further for even more accurate result that is comparable to the performance of true relevance feedback.",
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],
"normalizedAbstract": "Search context is a crucial factor that helps to understand a user’s information need in ad-hoc Web page retrieval. A query log of a search engine contains rich information on issued queries and their corresponding clicked Web pages. The clicked data implies its relevance to the query and can be used to define the topical context. However, the log is usually not completely available due to privacy concerns. In this paper, we derive clicked pages from clicked domains and use the surrounding query context to enhance retrieval performance. One strategy is to promote clicked pages directly in the initial retrieval result. Another strategy is to expand the original query using selected terms from the clicked pages. Our experimental results on the TREC GOV2 data and a query log of a major search engine show that both strategies can boost retrieval performance compared to the standard language model and pseudo relevance feedback (PRF) model. Their good performance on early precision allows us to apply PRF further for even more accurate result that is comparable to the performance of true relevance feedback.",
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"keywords": [
"Web Information Retrieval",
"Language Models",
"Parsimonious Language Models",
"Query Expansion",
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{
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"fullName": "Rongmei Li",
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"pubType": "proceedings",
"pages": "393-397",
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