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"abstract": "Protein secondary structure prediction is still a challenging task in bioinformatics, especially for 8-state (Q8) classification. To address this problem, we have proposed a deep learning based model by integrating graph convolutional network(GCN) and bidirectional long short-term memory (Bi-LSTM) network in this paper. In the model, GCN is utilized to synthesize the information of amino acids and their interactions, while Bi-LSTM has strong ability to capture the long-range dependencies of amino acids. For sequence representation, a new protein embedding derived by ProtTrans is used instead of the traditional amino acid one-hot encoding, together with evolutionary features of PSSM and HHM profiles. Amino acid contact potential derived from SPOTContact-Helical is used to construct amino acid graph. To verify the effectiveness of our proposed model, it is applied to several benchmark datasets, and obtained 78.05%, 76.81% 72.84%, 74.46% and 76.04% Q8 accuracy on CASP10, CASP11, CASP12, CB513 and TS115 datasets, respectively. Compared with 8 state-of-the-art competitions, our model obtained the best performance in most of datasets.",
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"abstract": "Computational fluid dynamics (CFD) study of the blood flow in the coiled cerebral aneurysm is one of powerful tools to explore the treatment mechanism of the endovascular coiling. Although several computational techniques were proposed to represent the realistic coil configuration in the aneurysm for the CFD studies, the CFD methodology for the blood flow analysis in the densely coiled aneurysm is still not fully discussed. The present study develops a CFD approach for the blood flow analysis in the densely coiled cerebral aneurysm without unstructured volume mesh creation. Patient-specific aneurysm geometry with realistic coil configuration was implicitly represented in a Cartesian-grid by using the volume of fraction (VOF) function. A Cartesian-grid CFD simulation was conducted in a finite difference manner with using the VOF function by the method of Weymouth and Yue (J. Compt. Phys., 2011). We conducted that two cases of numerical example of the blood flow analysis in the aneurysm prior to the coiling and after coiling with the packing density of 27%. These examples clearly exhibited that the developed CFD framework successfully resolved the fine flow characteristics around coils in the aneurysm despite the absence of explicit coil surface.",
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"content": "Computational fluid dynamics (CFD) study of the blood flow in the coiled cerebral aneurysm is one of powerful tools to explore the treatment mechanism of the endovascular coiling. Although several computational techniques were proposed to represent the realistic coil configuration in the aneurysm for the CFD studies, the CFD methodology for the blood flow analysis in the densely coiled aneurysm is still not fully discussed. The present study develops a CFD approach for the blood flow analysis in the densely coiled cerebral aneurysm without unstructured volume mesh creation. Patient-specific aneurysm geometry with realistic coil configuration was implicitly represented in a Cartesian-grid by using the volume of fraction (VOF) function. A Cartesian-grid CFD simulation was conducted in a finite difference manner with using the VOF function by the method of Weymouth and Yue (J. Compt. Phys., 2011). We conducted that two cases of numerical example of the blood flow analysis in the aneurysm prior to the coiling and after coiling with the packing density of 27%. These examples clearly exhibited that the developed CFD framework successfully resolved the fine flow characteristics around coils in the aneurysm despite the absence of explicit coil surface.",
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"abstract": "Diffusion tensor imaging (DTI) is widely used to characterize white matter in health and disease. Previous approaches to the estimation of diffusion tensors have either been statistically suboptimal or have used Gaussian approximations of the underlying noise structure, which is Rician in reality. This can cause quantities derived from these tensors — e.g., fractional anisotropy and apparent diffusion coefficient — to diverge from their true values, potentially leading to artifactual changes that confound clinically significant ones. This paper presents a novel maximum likelihood approach to tensor estimation, denoted Diffusion Tensor Estimation by Maximizing Rician Likelihood (DTEMRL). In contrast to previous approaches, DTEMRL considers the joint distribution of all observed data in the context of an augmented tensor model to account for variable levels of Rician noise. To improve numeric stability and prevent non-physical solutions, DTEMRL incorporates a robust characterization of positive definite tensors and a new estimator of underlying noise variance. In simulated and clinical data, mean squared error metrics show consistent and significant improvements from low clinical SNR to high SNR. DTEMRL may be readily supplemented with spatial regularization or a priori tensor distributions for Bayesian tensor estimation.",
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"content": "Diffusion tensor imaging (DTI) is widely used to characterize white matter in health and disease. Previous approaches to the estimation of diffusion tensors have either been statistically suboptimal or have used Gaussian approximations of the underlying noise structure, which is Rician in reality. This can cause quantities derived from these tensors — e.g., fractional anisotropy and apparent diffusion coefficient — to diverge from their true values, potentially leading to artifactual changes that confound clinically significant ones. This paper presents a novel maximum likelihood approach to tensor estimation, denoted Diffusion Tensor Estimation by Maximizing Rician Likelihood (DTEMRL). In contrast to previous approaches, DTEMRL considers the joint distribution of all observed data in the context of an augmented tensor model to account for variable levels of Rician noise. To improve numeric stability and prevent non-physical solutions, DTEMRL incorporates a robust characterization of positive definite tensors and a new estimator of underlying noise variance. In simulated and clinical data, mean squared error metrics show consistent and significant improvements from low clinical SNR to high SNR. DTEMRL may be readily supplemented with spatial regularization or a priori tensor distributions for Bayesian tensor estimation.",
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"abstractUrl": "/proceedings-article/fbit/2007/29990470/12OmNxFsmBW",
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"title": "2013 IEEE 24th International Conference on Application-Specific Systems, Architectures and Processors",
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"title": "Accelerating nonlinear diffusion tensor estimation for medical image processing using high performance GPU clusters",
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"abstract": "Diffusion Tensor Imaging (DTI) is a non-invasive magnetic resonance technique that produces in vivo images of biological tissues with local microstructural characteristics such as water diffusion. It can be used, for example, to localize white matter lesions, or in neuronavigation surgery of brain tumors. Diffusion tensor maps are usually computed on a voxel-by-voxel basis by fitting the signal intensities of diffusion weighted images as a function of their corresponding data acquisition parameters. This processing is highly computation-intensive and can be time-consuming which constraints the clinical use of DTI. This study presents the application of using high performance GPU clusters in diffusion tensor estimation by accelerating the multivariate non-linear regression. The results are tested in simulated DTI brain datasets and show significant performance gain in tensor fitting in addition to favorable scalability characteristics. The proposed GPU implementation framework can further promote the clinical use of DTI, and can be used to accelerate statistical analysis of DTI where Monte Carlo simulations are employed, or readily applied to quantitative assessment of DTI using bootstrap analysis.",
"abstracts": [
{
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"content": "Diffusion Tensor Imaging (DTI) is a non-invasive magnetic resonance technique that produces in vivo images of biological tissues with local microstructural characteristics such as water diffusion. It can be used, for example, to localize white matter lesions, or in neuronavigation surgery of brain tumors. Diffusion tensor maps are usually computed on a voxel-by-voxel basis by fitting the signal intensities of diffusion weighted images as a function of their corresponding data acquisition parameters. This processing is highly computation-intensive and can be time-consuming which constraints the clinical use of DTI. This study presents the application of using high performance GPU clusters in diffusion tensor estimation by accelerating the multivariate non-linear regression. The results are tested in simulated DTI brain datasets and show significant performance gain in tensor fitting in addition to favorable scalability characteristics. The proposed GPU implementation framework can further promote the clinical use of DTI, and can be used to accelerate statistical analysis of DTI where Monte Carlo simulations are employed, or readily applied to quantitative assessment of DTI using bootstrap analysis.",
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"normalizedAbstract": "Diffusion Tensor Imaging (DTI) is a non-invasive magnetic resonance technique that produces in vivo images of biological tissues with local microstructural characteristics such as water diffusion. It can be used, for example, to localize white matter lesions, or in neuronavigation surgery of brain tumors. Diffusion tensor maps are usually computed on a voxel-by-voxel basis by fitting the signal intensities of diffusion weighted images as a function of their corresponding data acquisition parameters. This processing is highly computation-intensive and can be time-consuming which constraints the clinical use of DTI. This study presents the application of using high performance GPU clusters in diffusion tensor estimation by accelerating the multivariate non-linear regression. The results are tested in simulated DTI brain datasets and show significant performance gain in tensor fitting in addition to favorable scalability characteristics. The proposed GPU implementation framework can further promote the clinical use of DTI, and can be used to accelerate statistical analysis of DTI where Monte Carlo simulations are employed, or readily applied to quantitative assessment of DTI using bootstrap analysis.",
"fno": "06567587",
"keywords": [
"Tensile Stress",
"Graphics Processing Units",
"Diffusion Tensor Imaging",
"Estimation",
"Acceleration",
"Mathematical Model",
"Scalability",
"High Performance Clusters",
"Diffusion Tensor Imaging DTI",
"Nonlinear Diffusion Tensor Estimation",
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"authors": [
{
"affiliation": "Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC 20064, USA",
"fullName": "Vinh Q. Dang",
"givenName": "Vinh Q.",
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{
"affiliation": "Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC 20064, USA",
"fullName": "Esam El-Araby",
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{
"affiliation": "Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC 20064, USA",
"fullName": "Lam H. Dao",
"givenName": "Lam H.",
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{
"affiliation": "Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC 20064, USA",
"fullName": "Lin-Ching Chang",
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"proceeding": {
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"title": "2007 Frontiers in the Convergence of Bioscience and Information Technologies (FBIT '07)",
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"doi": "10.1109/FBIT.2007.52",
"title": "Brain Differences Visualized in the Blind Using Tensor Manifold Statistics and Diffusion Tensor Imaging",
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"abstract": "Diffusion tensor magnetic resonance imaging (DTI) reveals the local orientation and integrity of white matter fiber structure based on imaging multidirectional water diffusion. Group differences in DTI images are often computed from single scalar measures, e.g., the Fractional Anisotropy (FA), discarding much of the information in the 6-parameter symmetric diffusion tensor. Here, we compute multivariate 6D tensor statistics to detect brain morphological changes in 12 blind subjects versus 14 sighted controls. After Log-Euclidean tensor de- noising, images were fluidly registered to a common template. Fluidly-convected tensor signals were re-oriented by applying the local rotational and translational component of the deformation. Since symmetric, positive- definite matrices form a non-Euclidean manifold, we applied a Riemannian manifold version of the Hotelling's T2 test to the logarithms of the tensors, using a log- Euclidean metric. Statistics on the full 6D tensor-valued images outperformed univariate analysis of scalar images, such as the FA and the geodesic anisotropy (GA).",
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"abstractType": "Regular",
"content": "Diffusion tensor magnetic resonance imaging (DTI) reveals the local orientation and integrity of white matter fiber structure based on imaging multidirectional water diffusion. Group differences in DTI images are often computed from single scalar measures, e.g., the Fractional Anisotropy (FA), discarding much of the information in the 6-parameter symmetric diffusion tensor. Here, we compute multivariate 6D tensor statistics to detect brain morphological changes in 12 blind subjects versus 14 sighted controls. After Log-Euclidean tensor de- noising, images were fluidly registered to a common template. Fluidly-convected tensor signals were re-oriented by applying the local rotational and translational component of the deformation. Since symmetric, positive- definite matrices form a non-Euclidean manifold, we applied a Riemannian manifold version of the Hotelling's T2 test to the logarithms of the tensors, using a log- Euclidean metric. Statistics on the full 6D tensor-valued images outperformed univariate analysis of scalar images, such as the FA and the geodesic anisotropy (GA).",
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"Tensor Manifold Statistics",
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"Fractional Anisotropy",
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"Statistics",
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"abstract": "This paper presents a method to simultaneously regularize diffusion weighted images and their estimated diffusion tensors, with the goal of suppressing noise and restoring tensor information. We enforce a data fidelity constraint, using coupled robust anisotropic diffusion filters, to ensure consistency of the restored diffusion tensors with the regularized diffusion weighted images. The filters are designed to take advantage of robust statistics and to be adopted to the anisotropic nature of diffusion tensors, which can effectively keep boundaries between piecewise constant regions in the tensor volume and also the diffusion weighted images during the regularized process. To facilitate Euclidean operations on the diffusion tensors, log-Euclidean metrics are adopted when performing the filtering. Experimental results on simulated and real image data demonstrate the effectiveness of the proposed method.",
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"affiliation": "Department of Radiology, University of North Carolina at Chapel Hill, 27599, USA",
"fullName": "Songyuan Tang",
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"affiliation": "Department of Radiology, University of North Carolina at Chapel Hill, 27599, USA",
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"affiliation": "Department of Biostatistics and BRIC, University of North Carolina at Chapel Hill, 27599, USA",
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"abstract": "The partial pattern matching is fundamental for pattern recognition to compare the pair of input patterns by exploiting the common features shared by those patterns while excluding the irrelevant ones. In this paper, for the pattern matching, we propose a novel method of smoothly structured sparse canonical correlation analysis, called S3CCA. The proposed method works on the feature matrix composed of a (local) feature dimension and an array dimension. In the framework of CCA, the method provides map weights along the array dimension to depict the parts that exhibit the common/similar features across the pair of feature matrices. By introducing the appropriate regularization into CCA, the map weights are optimized so as to be both smooth and localized, i.e., structured sparse. Thereby, the common features are effectively detected by the smooth and well-localized weights to improve the matching performance. In the experiments on pattern matching as well as classification based on the matching, the proposed method produces the favorable performance compared to the other methods.",
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"content": "The partial pattern matching is fundamental for pattern recognition to compare the pair of input patterns by exploiting the common features shared by those patterns while excluding the irrelevant ones. In this paper, for the pattern matching, we propose a novel method of smoothly structured sparse canonical correlation analysis, called S3CCA. The proposed method works on the feature matrix composed of a (local) feature dimension and an array dimension. In the framework of CCA, the method provides map weights along the array dimension to depict the parts that exhibit the common/similar features across the pair of feature matrices. By introducing the appropriate regularization into CCA, the map weights are optimized so as to be both smooth and localized, i.e., structured sparse. Thereby, the common features are effectively detected by the smooth and well-localized weights to improve the matching performance. In the experiments on pattern matching as well as classification based on the matching, the proposed method produces the favorable performance compared to the other methods.",
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"normalizedAbstract": "The partial pattern matching is fundamental for pattern recognition to compare the pair of input patterns by exploiting the common features shared by those patterns while excluding the irrelevant ones. In this paper, for the pattern matching, we propose a novel method of smoothly structured sparse canonical correlation analysis, called S3CCA. The proposed method works on the feature matrix composed of a (local) feature dimension and an array dimension. In the framework of CCA, the method provides map weights along the array dimension to depict the parts that exhibit the common/similar features across the pair of feature matrices. By introducing the appropriate regularization into CCA, the map weights are optimized so as to be both smooth and localized, i.e., structured sparse. Thereby, the common features are effectively detected by the smooth and well-localized weights to improve the matching performance. In the experiments on pattern matching as well as classification based on the matching, the proposed method produces the favorable performance compared to the other methods.",
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"abstract": "Pattern matching with wildcards is very important in many fields such as information retrieval and bioinformatics. Suffix trees are used in pattern matching with variable length wildcards. But the construction of a suffix tree needs significant time and space overload. This paper presents a new pattern matching algorithm, PST, based on multiple suffix trees. The PST algorithm uses a cutting process to divide a string S into several parts firstly, and then establishes a suffix tree for each part of S respectively. If multiple patterns are to be retrieved, the suffix trees should be adjusted according to the cutting points: prefix sequence deletion and suffix sequence addition; prefix sequence addition and suffix sequence deletion. Theoretical analysis and experiments show that the PST algorithm can decrease the time and space overload than other peers.",
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"abstract": "A classic open problem on tree pattern matching is whether the naive O(mn)-step algorithm for finding all the occurrences of a pattern tree of size m in a text tree of size n can be improved. An O(nM/sup 0.75/ polylog(m))-step algorithm for this tree pattern matching problem is designed. The problems of linear string matching with don't care symbols and linear string max-min convolution are treated.",
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"abstract": "Pattern matching is an important task, which is widely used in many fields, such as information retrieval and bioinformatics. Recently, a much more flexible pattern matching problem with wildcards has been proposed. Chen et al. introduced local constraints, global constraints and the one-off condition into the task of pattern matching, and the most representative algorithm SAIL was designed. However, the performance of SAIL is not analyzed well, which affects its application. Therefore, this paper analyzes the performance of SAIL in-depth, and discovers that the matching result is closely related to the features of patterns. Meanwhile, the completeness of SAIL in the pattern matching with no-recurring characters is proved, and an improved algorithm, named RSAIL, is proposed for pattern matching with recurring tail characters. Extensive experiments demonstrate that RSAIL improves the number of matches by 2.2% compared to SAIL.",
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"abstract": "The extraction of rotation invariant representation is important for many signal processing problems such as image analysis, computer vision, and pattern recognition. In this paper, we present a systematic analysis of the Two-Dimensional Fractional Fourier Transform (2D-FRFT), and show that under certain conditions, the 2D-FRFT technique possesses the attractive property of rotation invariance. Based on our analysis, we proposed a novel digital image watermarking method which combines 2D chirp signal with the addition and rotation invariant properties of 2D-FRFT to achieve improved robustness and security. The effectiveness of the proposed solution is demonstrated through experiments.",
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"title": "2017 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)",
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"doi": "10.1109/ICCAIRO.2017.39",
"title": "Development of Permanent Magnet Magnetic Field System of Vibroactuatror for Prosthetic Feedback Devices",
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"abstract": "According to World Health Organization data about one billion of people worldwide are disabled people. About one third of these people are people with mobility or movements impairments. First problem is that for that kind people high precision prosthetic devices with neural control and feedback are very expensive. Second problem almost all average price prosthetic devices have visual feedback system which loads user vision. To solve these two problems decided develop prosthetic devices with other kind of feedback systems: thermal, electrical, pressure, vibration...etc. For using vibration feedback, system vibration motors must work in wide range of frequency spectrum and produce controlled intensity in every frequency point. Unfortunately, nowadays-produced vibration motors cannot satisfy these requirements. In this paper represented 3d-printed voice-coil motor (VCM) magnetic circuit parameters changes depended on additional axial and radial ferromagnetic components and without them. Developed and tested VCM prototypes with different radial and axial ferromagnetic placement. Main measured parameters are magnetic flux density and derived current-force dependence linear domain. The best solution is without any iron shields. Axial shields has no effect on the induction, radial shield shunt magnetic field out of working zone of voice coil of the VCM.",
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"keywords": [
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"Feedback",
"Handicapped Aids",
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"Permanent Magnets",
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"High Precision Prosthetic Devices",
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"Axial Ferromagnetic Placement",
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"Iron",
"Magnetic Domains",
"Haptic Interfaces",
"Permanent Magnets",
"Vibrations",
"Permanent Magnet Machines",
"Vibro Tactile Devices",
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"proceeding": {
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"title": "2017 IEEE 67th Electronic Components and Technology Conference (ECTC)",
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"doi": "10.1109/ECTC.2017.54",
"title": "Large Scale Cryogenic Integration Approach for Superconducting High-Performance Computing",
"normalizedTitle": "Large Scale Cryogenic Integration Approach for Superconducting High-Performance Computing",
"abstract": "Superconducting single-flux-quantum-based (SFQ) digital integrated circuits (ICs) are a promising candidate for high-speed and ultralow energy dissipation computing systems. Circuits based on several versions of SFQ-type logic, RQL, and AQFP logic have been demonstrated with complexities reaching up to a few tens of thousands of gates. Packaging many superconducting ICs using microbump (15μm or less) technology and performing high throughput, nearly lossless data transfer between various superconducting and/or CMOS chips are highly desirable for a hybrid superconducting computer architecture, but this density has not yet been demonstrated. An efficient way to achieve this goal is to couple the chips through a passive superconductive multichip module (S-MCM) that distributes information between integrated circuits utilizing lossless superconducting transmission lines. Here we show implementation of such a superconducting base using well-defined impedance lines to couple multiple ICs to enable a cryogenic integration approach for possible future hybrid superconducting computing systems. The use of indium-based microbumps to form interconnects between an S-MCM and superconducting ICs, and its electrical performance, are discussed. Optimized microbumps were used to fabricate interconnections on a large S-MCM by using thermocompression(TC) bonding. From 17200 to 68800 microbumps fabricated in a 5 mm × 5 mm active area of an S-MCM, having pitches ranging from 35 μm to 15 μm, were bonded with a superconducting IC to create a daisy chain structure. In addition, a large active area (10 mm × 10 mm) flip-chip having continuous niobium daisy chains with up to 77,500 bumps with 15 μm bump diameter and 35 μm pitch was also demonstrated. As a case study, an optimized microbump interconnect construction for attaching 16 superconducting chips with a large 32 mm × 32 mm superconducting base was fabricated and tested at room temperature and 4.2 K to study the structural and electrical integrity. Josephson junctions and niobium lines on integrated superconducting chips and the superconducting base maintained their I-V characteristics, which allows the design of building blocks for a superconducting computing system.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Superconducting single-flux-quantum-based (SFQ) digital integrated circuits (ICs) are a promising candidate for high-speed and ultralow energy dissipation computing systems. Circuits based on several versions of SFQ-type logic, RQL, and AQFP logic have been demonstrated with complexities reaching up to a few tens of thousands of gates. Packaging many superconducting ICs using microbump (15μm or less) technology and performing high throughput, nearly lossless data transfer between various superconducting and/or CMOS chips are highly desirable for a hybrid superconducting computer architecture, but this density has not yet been demonstrated. An efficient way to achieve this goal is to couple the chips through a passive superconductive multichip module (S-MCM) that distributes information between integrated circuits utilizing lossless superconducting transmission lines. Here we show implementation of such a superconducting base using well-defined impedance lines to couple multiple ICs to enable a cryogenic integration approach for possible future hybrid superconducting computing systems. The use of indium-based microbumps to form interconnects between an S-MCM and superconducting ICs, and its electrical performance, are discussed. Optimized microbumps were used to fabricate interconnections on a large S-MCM by using thermocompression(TC) bonding. From 17200 to 68800 microbumps fabricated in a 5 mm × 5 mm active area of an S-MCM, having pitches ranging from 35 μm to 15 μm, were bonded with a superconducting IC to create a daisy chain structure. In addition, a large active area (10 mm × 10 mm) flip-chip having continuous niobium daisy chains with up to 77,500 bumps with 15 μm bump diameter and 35 μm pitch was also demonstrated. As a case study, an optimized microbump interconnect construction for attaching 16 superconducting chips with a large 32 mm × 32 mm superconducting base was fabricated and tested at room temperature and 4.2 K to study the structural and electrical integrity. Josephson junctions and niobium lines on integrated superconducting chips and the superconducting base maintained their I-V characteristics, which allows the design of building blocks for a superconducting computing system.",
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"fno": "07999762",
"keywords": [
"Cryogenic Electronics",
"Integrated Circuit Packaging",
"Multichip Modules",
"Superconducting Logic Circuits",
"Tape Automated Bonding",
"Large Scale Cryogenic Integration Approach",
"Superconducting High Performance Computing",
"Superconducting Single Flux Quantum Based Digital Integrated Circuits",
"Ultralow Energy Dissipation Computing Systems",
"SFQ Type Logic",
"RQL",
"AQFP Logic",
"Superconducting IC",
"Microbump Technology",
"Hybrid Superconducting Computer Architecture",
"Passive Superconductive Multichip Module",
"Lossless Superconducting Transmission Lines",
"Hybrid Superconducting Computing Systems",
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"Thermocompression Bonding",
"Daisy Chain Structure",
"Flip Chip",
"Optimized Microbump Interconnect Construction",
"Superconducting Chips",
"Structural Integrity",
"Electrical Integrity",
"Josephson Junctions",
"Niobium Lines",
"Superconducting Integrated Circuits",
"Josephson Junctions",
"Superconducting Epitaxial Layers",
"Superconducting Transmission Lines",
"Integrated Circuit Interconnections",
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"Bonding",
"S MCM",
"Microbump",
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"affiliation": null,
"fullName": "Rabindra N. Das",
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{
"affiliation": null,
"fullName": "Vladimir Bolkhovsky",
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{
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"fullName": "Sergey K Tolpygo",
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"fullName": "Pascale Gouker",
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"fullName": "Eric A. Dauler",
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"title": "SC21: International Conference for High Performance Computing, Networking, Storage and Analysis",
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"doi": "10.1145/3458817.3487398",
"title": "Symplectic Structure-Preserving Particle-in-Cell Whole-Volume Simulation of Tokamak Plasmas to 111.3 Trillion Particles and 25.7 Billion Grids",
"normalizedTitle": "Symplectic Structure-Preserving Particle-in-Cell Whole-Volume Simulation of Tokamak Plasmas to 111.3 Trillion Particles and 25.7 Billion Grids",
"abstract": "We employ our recently developed explicit 2nd-order charge-conservative symplectic electromagnetic particle-in-cell (PIC) scheme in the cylindrical mesh to simulate the whole-volume magnetic confinement toroidal plasmas on the new Sunway supercomputer. From a large-scale simulation of magneticized toroidal plasma with 111.3 trillion particles and 25.7 billion grids, we have obtained a sustained performance exceeding 201.1 PFLOP/s (double precision) with the fastest iteration step achieving 298.2 PFLOP/s (double precision). For the first time, unprecedented high resolution evolution of 6D electromagnetic fully kinetic plasmas based on 2D equilibrium profiles from Experimental Advanced Superconducting Tokamak (EAST) and designed operation state of China Fusion Engineering Test Reactor (CFETR) are presented, and edge micro-instabilities can be investigated directly. This shows the possibility to study crucial problems and phenomena in the magnetic confinement toroidal plasma directly using the symplectic electromagnetic fully kinetic PIC method on world's leading supercomputers.",
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{
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"content": "We employ our recently developed explicit 2nd-order charge-conservative symplectic electromagnetic particle-in-cell (PIC) scheme in the cylindrical mesh to simulate the whole-volume magnetic confinement toroidal plasmas on the new Sunway supercomputer. From a large-scale simulation of magneticized toroidal plasma with 111.3 trillion particles and 25.7 billion grids, we have obtained a sustained performance exceeding 201.1 PFLOP/s (double precision) with the fastest iteration step achieving 298.2 PFLOP/s (double precision). For the first time, unprecedented high resolution evolution of 6D electromagnetic fully kinetic plasmas based on 2D equilibrium profiles from Experimental Advanced Superconducting Tokamak (EAST) and designed operation state of China Fusion Engineering Test Reactor (CFETR) are presented, and edge micro-instabilities can be investigated directly. This shows the possibility to study crucial problems and phenomena in the magnetic confinement toroidal plasma directly using the symplectic electromagnetic fully kinetic PIC method on world's leading supercomputers.",
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],
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{
"affiliation": "University of Science and Technology of China,Hefei,China",
"fullName": "Jianyuan Xiao",
"givenName": "Jianyuan",
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{
"affiliation": "University of Science and Technology of China,Hefei,China",
"fullName": "Junshi Chen",
"givenName": "Junshi",
"surname": "Chen",
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{
"affiliation": "University of Science and Technology of China,Hefei,China",
"fullName": "Jiangshan Zheng",
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"surname": "Zheng",
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{
"affiliation": "University of Science and Technology of China,Hefei,China",
"fullName": "Hong An",
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"surname": "An",
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{
"affiliation": "University of Science and Technology of China,Hefei,China",
"fullName": "Shenghong Huang",
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{
"affiliation": "School of Mathematical Sciences, Peking University,Beijing,China",
"fullName": "Chao Yang",
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"affiliation": "National Supercomputing Center in Wuxi,Wuxi,China",
"fullName": "Fang Li",
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"surname": "Li",
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{
"affiliation": "University of Science and Technology of China,Hefei,China",
"fullName": "Ziyu Zhang",
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"affiliation": "University of Science and Technology of China,Hefei,China",
"fullName": "Yeqi Huang",
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"affiliation": "University of Science and Technology of China,Hefei,China",
"fullName": "Wenting Han",
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"fullName": "Xin Liu",
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"fullName": "Dexun Chen",
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"affiliation": "University of Science and Technology of China,Hefei,China",
"fullName": "Zixi Liu",
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"affiliation": "University of Science and Technology of China,Hefei,China",
"fullName": "Ge Zhuang",
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"affiliation": "Institute of Plasma Physics, Chinese Academy of Sciences,Hefei,China",
"fullName": "Jiale Chen",
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"affiliation": "Institute of Plasma Physics, Chinese Academy of Sciences,Hefei,China",
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"fullName": "Xuan Sun",
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"title": "2020 ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP)",
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"title": "Global Interconnects in VLSI Complexity Single Flux Quantum Systems",
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"abstract": "On-chip signal routing has become an issue of growing importance in modern VLSI complexity single flux quantum (SFQ) systems. In this paper, different routing methods for these systems are described. The routing methods include either passive transmission lines (PTLs) or Josephson transmission lines (JTLs) as interconnects. Driving multiple SFQ gates is also a challenging issue in automated layout and clock tree synthesis (CTS) due to the limited fanout of SFQ gates. To support multiple fanout, splitters are used to distribute multiple SFQ pulses. These splitters require significant area, delay, and power. In this paper, several area and power efficient splitters are proposed for large scale SFQ integrated circuits. A primary issue within a long SFQ interconnect is resonance effects due to the imperfect match between the PTLs and Josephson junctions. A repeater insertion methodology for long interconnect to reduce and manage these resonance effects is also described. Summarizing, guidelines and tradeoffs appropriate for automated layout and synthesis are described for driving long and short interconnect in VLSI complexity SFQ systems.",
"abstracts": [
{
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"content": "On-chip signal routing has become an issue of growing importance in modern VLSI complexity single flux quantum (SFQ) systems. In this paper, different routing methods for these systems are described. The routing methods include either passive transmission lines (PTLs) or Josephson transmission lines (JTLs) as interconnects. Driving multiple SFQ gates is also a challenging issue in automated layout and clock tree synthesis (CTS) due to the limited fanout of SFQ gates. To support multiple fanout, splitters are used to distribute multiple SFQ pulses. These splitters require significant area, delay, and power. In this paper, several area and power efficient splitters are proposed for large scale SFQ integrated circuits. A primary issue within a long SFQ interconnect is resonance effects due to the imperfect match between the PTLs and Josephson junctions. A repeater insertion methodology for long interconnect to reduce and manage these resonance effects is also described. Summarizing, guidelines and tradeoffs appropriate for automated layout and synthesis are described for driving long and short interconnect in VLSI complexity SFQ systems.",
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"affiliation": "Department of Electrical and Computer Engineering University of Rochester,Rochester,New York,14627",
"fullName": "Tahereh Jabbari",
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"affiliation": "Department of Electrical and Computer Engineering University of Rochester,Rochester,New York,14627",
"fullName": "Eby G. Friedman",
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"title": "Online multi-modal task-driven dictionary learning and robust joint sparse representation for visual tracking",
"normalizedTitle": "Online multi-modal task-driven dictionary learning and robust joint sparse representation for visual tracking",
"abstract": "Robust visual tracking is a challenging problem due to pose variance, occlusion and cluttered backgrounds. No single feature can be robust to all possible scenarios in a video sequence. However, exploiting multiple features has demonstrated its effectiveness in overcoming challenging situations in visual tracking. We propose a new framework for multi-modal fusion at both the feature level and decision level by training a reconstructive and discriminative dictionary and classifier for each modality simultaneously with the additional constraint of label consistency across different modalities. In addition, a joint decision measure is designed based on both reconstruction and classification error to adaptively adjust the weights of different features such that unreliable features can be removed from tracking. The proposed tracking scheme is referred to as the label-consistent and fusion-based joint sparse coding (LC-FJSC). Extensive experiments on publicly available videos demonstrate that LC-FJSC outperforms state-of-the-art trackers.",
"abstracts": [
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"content": "Robust visual tracking is a challenging problem due to pose variance, occlusion and cluttered backgrounds. No single feature can be robust to all possible scenarios in a video sequence. However, exploiting multiple features has demonstrated its effectiveness in overcoming challenging situations in visual tracking. We propose a new framework for multi-modal fusion at both the feature level and decision level by training a reconstructive and discriminative dictionary and classifier for each modality simultaneously with the additional constraint of label consistency across different modalities. In addition, a joint decision measure is designed based on both reconstruction and classification error to adaptively adjust the weights of different features such that unreliable features can be removed from tracking. The proposed tracking scheme is referred to as the label-consistent and fusion-based joint sparse coding (LC-FJSC). Extensive experiments on publicly available videos demonstrate that LC-FJSC outperforms state-of-the-art trackers.",
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"affiliation": "Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, 1520 Middle Drive, United States",
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"affiliation": "Electrical and Computer Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL 33124, United States",
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"abstract": "As spatio-temporal data have become ubiquitous, an increasing challenge facing computer scientists is that of identifying discrete patterns in continuous spatio-temporal fields. In this paper, we introduce a parameter-free pattern mining application that is able to identify dynamic anomalies in ocean data, known as ocean eddies. Despite ocean eddy monitoring being an active field of research, we provide one of the first quantitative analyses of the performance of the most used monitoring algorithms. We present an incomplete information validation technique, that uses the performance of two methods to construct an imperfect ground truth to test the significance of patterns discovered as well as the relative performance of pattern mining algorithms. These methods, in addition to the validation schemes discussed provide researchers new directions in analyzing large unlabeled climate datasets.",
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"abstract": "High-resolution climate simulations are increasingly in demand and require tremendous computing resources. In the Community Earth SystemModel (CESM), the Parallel Ocean Model (POP) is computationally expensive for high-resolution grids (e.g., 0.1°) and is frequently the least scalable component of CESM for certain production simulations. In particular, the modified Preconditioned Conjugate Gradient (PCG), used to solve the elliptic system of equations in the barotropic mode, scales poorly at the high core counts, which is problematic for high-resolution simulations. In this work, we demonstrate that the communication costs in the barotropic solver occupy an increasing portion of the total POP execution time as core counts are increased. To mitigate this problem, we implement a preconditioned Chebyshev-type iterative method in POP (called P-CSI), which requires far fewer global reductions than PCG. We also develop an effective block preconditioner based on the Error Vector Propagation Method to attain a competitive convergence rate for P-CSI. We demonstrate that the improved scalability of P-CSI results in a 5.2x speedup of the barotropic mode in high-resolution POP on 16,875 cores, which yields a 1.7x speedup of the overall POP simulation. Further, we ensure that the new solver produces an ocean climate consistent with the original one via an ensemble-based statistical method.",
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"abstract": "In order to meet the urgent need for mesoscale eddy data assimilation, here we built a model for dissymmetrical ocean mesoscale eddy. It adopted the e-folding radius and assumed a Gaussian decay of the eddy amplitude with horizontal distance from the centre. We didn't regarded the structure of eddy was symmetrical again and introduce the left radius and right radius on each layer. Thus the model can present the dissymmetry of eddy perfectly. We applied the model on the east china sea cold eddy and compared with the experiment data and remote sensing data, which proved the model was useful and effective. It can be used to assimilate remote sensing data and reconstruct the dissymmetrical eddies.",
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"content": "In order to meet the urgent need for mesoscale eddy data assimilation, here we built a model for dissymmetrical ocean mesoscale eddy. It adopted the e-folding radius and assumed a Gaussian decay of the eddy amplitude with horizontal distance from the centre. We didn't regarded the structure of eddy was symmetrical again and introduce the left radius and right radius on each layer. Thus the model can present the dissymmetry of eddy perfectly. We applied the model on the east china sea cold eddy and compared with the experiment data and remote sensing data, which proved the model was useful and effective. It can be used to assimilate remote sensing data and reconstruct the dissymmetrical eddies.",
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"abstract": "Synthetic Aperture Radar (SAR) provides a large amount of image data for the observation and research of ocean eddies. Using SAR images to detect ocean eddy targets is of great significance to the research of ocean eddies and the application of SAR eddy images. This paper proposes a complete process of ocean eddy detection based on SAR images. Combined with the characteristics of SAR images and ocean eddies, the SAR ocean eddy detection is transformed into the problem of eddy texture feature extraction, and a method of distinguishing and processing eddy images according to the judgment conditions is proposed to achieve the purpose of eddy detection. First, the SAR ocean eddy image is selectively pre-processed; secondly, the pre-processed eddy image is denoised based on the NSST total variation regularization method or the WNNM method; Then, the eddy feature extraction is performed on the denoised eddy image by using the wavelet transform modulo maximum method or the structural morphological method; Finally, the eddy image after feature extraction is post-image processed, and the detected ocean eddy target is marked on the original SAR image.",
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"content": "Synthetic Aperture Radar (SAR) provides a large amount of image data for the observation and research of ocean eddies. Using SAR images to detect ocean eddy targets is of great significance to the research of ocean eddies and the application of SAR eddy images. This paper proposes a complete process of ocean eddy detection based on SAR images. Combined with the characteristics of SAR images and ocean eddies, the SAR ocean eddy detection is transformed into the problem of eddy texture feature extraction, and a method of distinguishing and processing eddy images according to the judgment conditions is proposed to achieve the purpose of eddy detection. First, the SAR ocean eddy image is selectively pre-processed; secondly, the pre-processed eddy image is denoised based on the NSST total variation regularization method or the WNNM method; Then, the eddy feature extraction is performed on the denoised eddy image by using the wavelet transform modulo maximum method or the structural morphological method; Finally, the eddy image after feature extraction is post-image processed, and the detected ocean eddy target is marked on the original SAR image.",
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"affiliation": "Nanjing University of Science & Technology,Department of Communication Engineering,Nanjing,China",
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"abstract": "Mesoscale ocean eddies play a major role for both the intermixing of water and the transport of biological mass. This makes the identification and tracking of their shape, location and deformation over time highly important for a number of applications. While eddies maintain a roughly circular shape in the free ocean, the narrow basins of the Red Sea and Gulf of Aden lead to the formation of irregular eddy shapes that existing methods struggle to identify. We propose the following model: Inside an eddy, particles rotate around a common core and thereby remain at a constant distance under a certain parametrization. The transition to the more unpredictable flow on the outside can thus be identified as the eddy boundary. We apply this algorithm on a realistic simulation of the Red Sea circulation, where we are able to identify the shape of irregular eddies robustly and more coherently than previous methods. We visualize the eddies as tubes in space-time to enable the analysis of their movement and deformation over several weeks.",
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"content": "Mesoscale ocean eddies play a major role for both the intermixing of water and the transport of biological mass. This makes the identification and tracking of their shape, location and deformation over time highly important for a number of applications. While eddies maintain a roughly circular shape in the free ocean, the narrow basins of the Red Sea and Gulf of Aden lead to the formation of irregular eddy shapes that existing methods struggle to identify. We propose the following model: Inside an eddy, particles rotate around a common core and thereby remain at a constant distance under a certain parametrization. The transition to the more unpredictable flow on the outside can thus be identified as the eddy boundary. We apply this algorithm on a realistic simulation of the Red Sea circulation, where we are able to identify the shape of irregular eddies robustly and more coherently than previous methods. We visualize the eddies as tubes in space-time to enable the analysis of their movement and deformation over several weeks.",
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"title": "Mesoscale Eddy Detection Based on the Deep Learning Method",
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"abstract": "Mesoscale eddies play an essential role in ocean momentum, energy, heat, and mass transport. Thus, it can also significantly affect the distribution of nutrients, phytoplankton, as well as the shoal of fish. In order to quickly and accurately locate the mesoscale eddies in the ocean, we fused the velocity, divergence, vorticity, and Okubo-Weiss (OW) parameter information with the sea surface height anomalies (SSHA) data, respectively. Then, the feature pyramid network (FPN) in the deep learning method was adopted to extract the eddy position from fused information. The experiment shows that the proposed method can effectively detect the mesoscale eddy locations. Besides, it can also be concluded that, compared with the merged data with velocity and divergence, the FPN performs better on fused data with vorticity and OW information.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Mesoscale eddies play an essential role in ocean momentum, energy, heat, and mass transport. Thus, it can also significantly affect the distribution of nutrients, phytoplankton, as well as the shoal of fish. In order to quickly and accurately locate the mesoscale eddies in the ocean, we fused the velocity, divergence, vorticity, and Okubo-Weiss (OW) parameter information with the sea surface height anomalies (SSHA) data, respectively. Then, the feature pyramid network (FPN) in the deep learning method was adopted to extract the eddy position from fused information. The experiment shows that the proposed method can effectively detect the mesoscale eddy locations. Besides, it can also be concluded that, compared with the merged data with velocity and divergence, the FPN performs better on fused data with vorticity and OW information.",
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"affiliation": "National University of Défense Technology,Institute of Meteorology and Oceanology,Nanjing,China,211101",
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"abstract": "The colours used in a painting are determined by artists and the pigments at their disposal. Therefore, knowing who made the painting should help in determining which colours to hallucinate when given a colourless version of the painting. The main aim of this paper is to determine if we can create a colourisation model for paintings which generates artist-specific colourisations. Building on earlier work on natural-image colourisation, we propose a model capable of producing colourisations of paintings by incorporating a conditional normalisation scheme, i.e., conditional instance normalisation. The results indicate that a conditional normalisation scheme is beneficial to the performance. In addition, we compare the colourisations of our model that is trained on a large dataset of paintings, with those of competitive models trained on natural images and find that the painting-specific training is beneficial to the colourisation performance. Finally, we demonstrate the results of stylistic colour transfer experiments in which artist-specific colourisations are applied to the artworks of other artists. We conclude that painting colourisation is feasible and benefits from being trained on a dataset of paintings and from applying a conditional normalisation scheme.",
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"abstract": "We propose a novel framework for reconstructing lightweight polygonal surfaces from point clouds. Unlike traditional methods that focus on either extracting good geometric primitives or obtaining proper arrangements of primitives, the emphasis of this work lies in intersecting the primitives (planes only) and seeking for an appropriate combination of them to obtain a manifold polygonal surface model without boundary. We show that reconstruction from point clouds can be cast as a binary labeling problem. Our method is based on a hypothesizing and selection strategy. We first generate a reasonably large set of face candidates by intersecting the extracted planar primitives. Then an optimal subset of the candidate faces is selected through optimization. Our optimization is based on a binary linear programming formulation under hard constraints that enforce the final polygonal surface model to be manifold and watertight. Experiments on point clouds from various sources demonstrate that our method can generate lightweight polygonal surface models of arbitrary piecewise planar objects. Besides, our method is capable of recovering sharp features and is robust to noise, outliers, and missing data.",
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"abstract": "This paper aims to explore the optimal feature selection with dimensionality reduction and jointly sparse representation scheme for classification. The proposed method is called Optimal Feature Selection Classification (OFSC). Our model simultaneously learns an orthogonal subspace for jointly sparse feature selection and representation via l2,1-norms regularization. To solve the proposed model, an alternately iterative algorithm is proposed to optimize both the jointly sparse projection matrix and representation matrix. Experimental results on three public face datasets and one action dataset validate the quick convergence of our algorithm and show that the proposed method is more competitive than the state-of-the-art methods.",
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"abstract": "Customizable programs and program families provide user-selectable features to allow users to tailor a program to an application scenario. Knowing in advance which feature selection yields the best performance is difficult because a direct measurement of all possible feature combinations is infeasible. Our work aims at predicting program performance based on selected features. However, when features interact, accurate predictions are challenging. An interaction occurs when a particular feature combination has an unexpected influence on performance. We present a method that automatically detects performance-relevant feature interactions to improve prediction accuracy. To this end, we propose three heuristics to reduce the number of measurements required to detect interactions. Our evaluation consists of six real-world case studies from varying domains (e.g., databases, encoding libraries, and web servers) using different configuration techniques (e.g., configuration files and preprocessor flags). Results show an average prediction accuracy of 95%.",
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"content": "Over the last two decades, the advances in computer vision and pattern recognition power have opened the door to new opportunity of automatic facial expression recognition system In this work, we have introduced a new feature-based approach for facial expressions recognition. The proposed approach provides full automatic solution to identify human expressions as well as overcoming facial expressions variation and intensity problems. Facial features component were automatically detected and segmented. Then, we have detected facial feature points which go with facial expression deformations. Afterwards, distances between these points were computed and used through Data mining technique to generate a set of relevant prediction rules able to classify facial expressions. We took into account the intensity of JOY expression. Thus, we have defined SMILE expression as the lowest intensity of JOY. Seven facial expression classes were defined: JOY, SMILE, SURPRISE, DISGUST, ANGER, SADNESS, and FEAR We have appraised experimental study to evaluate the performance of the proposed solution.",
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"abstract": "Facial feature detection from facial images has attracted great attention in the field of computer vision. It is a nontrivial task since the appearance and shape of the face tend to change under different conditions. In this paper, we propose a hierarchical probabilistic model that could infer the true locations of facial features given the image measurements even if the face is with significant facial expression and pose. The hierarchical model implicitly captures the lower level shape variations of facial components using the mixture model. Furthermore, in the higher level, it also learns the joint relationship among facial components, the facial expression, and the pose information through automatic structure learning and parameter estimation of the probabilistic model. Experimental results on benchmark databases demonstrate the effectiveness of the proposed hierarchical probabilistic model.",
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"abstract": "Facial expression synthesis is an important part of the visual human-computer interaction. In order to establish a highly realistic, self-adaptive and automatic real-time facial expression synthesis system, this paper proposes a method based on MPEG-4 to generate three-dimensional facial expression animation. The key steps include: firstly, mark some feature points in three-dimensional human faces and evaluate how these points move from neutral to other facial expression to get animation parameters of these points, secondly, calculate the motility factor of non-feature points in meshed human faces using the interpolation algorithm in this paper and the animation parameters of feature points, lastly, simulate the movements of feature points along time axis to realize facial expression animation. The finally synthesized facial expression looks real and natural, which confirms the validity of the method proposed in this paper.",
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"normalizedAbstract": "Facial expression synthesis is an important part of the visual human-computer interaction. In order to establish a highly realistic, self-adaptive and automatic real-time facial expression synthesis system, this paper proposes a method based on MPEG-4 to generate three-dimensional facial expression animation. The key steps include: firstly, mark some feature points in three-dimensional human faces and evaluate how these points move from neutral to other facial expression to get animation parameters of these points, secondly, calculate the motility factor of non-feature points in meshed human faces using the interpolation algorithm in this paper and the animation parameters of feature points, lastly, simulate the movements of feature points along time axis to realize facial expression animation. The finally synthesized facial expression looks real and natural, which confirms the validity of the method proposed in this paper.",
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"title": "Automatic Synthesis of Realistic Facial Expressions",
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"abstract": "Facial expressions exhibit not only facial feature motions, but also subtle changes in illumination and appearance. Since it is difficult to generate realistic facial expressions by using only geometric deformations, the details such as textures should also be deformed to achieve expression that is more realistic. In this paper, we propose a nonlinear model for skin color change and a model-based synthesis method for facial expression. Experimental results show that the proposed method generates realistic facial expressions under various lighting conditions.",
"abstracts": [
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"content": "Facial expressions exhibit not only facial feature motions, but also subtle changes in illumination and appearance. Since it is difficult to generate realistic facial expressions by using only geometric deformations, the details such as textures should also be deformed to achieve expression that is more realistic. In this paper, we propose a nonlinear model for skin color change and a model-based synthesis method for facial expression. Experimental results show that the proposed method generates realistic facial expressions under various lighting conditions.",
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"title": "Towards real-time modeling and haptic rendering of deformable objects for point cloud-based Model-Mediated Teleoperation",
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"abstract": "We propose a novel radial function-based deformation (RFBD) approach to enable real-time modeling and haptic rendering of deformable objects for point cloud-based Model-Mediated Teleoperation (pcbMMT). In the pcbMMT system, a time-of-flight camera is employed on the slave side to capture a 3D point cloud model of the remote environment. This environment model is transmitted to the master along with the estimated model parameters, including the dynamics of the object's surface deformation and the physical properties such as the stiffness, friction coefficient, etc. Due to the simplicity of the RFBD approach, the model parameters of the remote environment can be obtained in real time. On the master side, a local copy of the environment model is maintained according to the received parameters. Both the haptic rendering and deformation simulation are based on this local model, running at 1kHz. Thus, a good compromise is achieved between the model accuracy and the computational time for online parameter identification. Experiments verify the feasibility of the proposed approach, and show the results of the object deformation for different model parameters.",
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"content": "We propose a novel radial function-based deformation (RFBD) approach to enable real-time modeling and haptic rendering of deformable objects for point cloud-based Model-Mediated Teleoperation (pcbMMT). In the pcbMMT system, a time-of-flight camera is employed on the slave side to capture a 3D point cloud model of the remote environment. This environment model is transmitted to the master along with the estimated model parameters, including the dynamics of the object's surface deformation and the physical properties such as the stiffness, friction coefficient, etc. Due to the simplicity of the RFBD approach, the model parameters of the remote environment can be obtained in real time. On the master side, a local copy of the environment model is maintained according to the received parameters. Both the haptic rendering and deformation simulation are based on this local model, running at 1kHz. Thus, a good compromise is achieved between the model accuracy and the computational time for online parameter identification. Experiments verify the feasibility of the proposed approach, and show the results of the object deformation for different model parameters.",
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"abstract": "Previous works have presented solutions for stability problems arising from the difference between the sampling rate requirements for haptic devices (about 1 KHz) and the update rates of the physic alobjects being simulated (about 10 Hz). These methods work well when the objects are convex and non-deformable but when the object is deformable these methods might fail in obtaining realistic force feedback and exact graphical rendering. The reason of this is due to the concavities and unknown shapes that may appear in the deformable objects. This paper proposes to make the haptic interaction with the local topology of the object and therefore taking into account the unknown changes and the concavities in the object shape. This local model will be updated at the simulation frequency rate.",
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"content": "Previous works have presented solutions for stability problems arising from the difference between the sampling rate requirements for haptic devices (about 1 KHz) and the update rates of the physic alobjects being simulated (about 10 Hz). These methods work well when the objects are convex and non-deformable but when the object is deformable these methods might fail in obtaining realistic force feedback and exact graphical rendering. The reason of this is due to the concavities and unknown shapes that may appear in the deformable objects. This paper proposes to make the haptic interaction with the local topology of the object and therefore taking into account the unknown changes and the concavities in the object shape. This local model will be updated at the simulation frequency rate.",
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"normalizedAbstract": "Previous works have presented solutions for stability problems arising from the difference between the sampling rate requirements for haptic devices (about 1 KHz) and the update rates of the physic alobjects being simulated (about 10 Hz). These methods work well when the objects are convex and non-deformable but when the object is deformable these methods might fail in obtaining realistic force feedback and exact graphical rendering. The reason of this is due to the concavities and unknown shapes that may appear in the deformable objects. This paper proposes to make the haptic interaction with the local topology of the object and therefore taking into account the unknown changes and the concavities in the object shape. This local model will be updated at the simulation frequency rate.",
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"abstract": "Simplification of scattered point cloud is one of the key preprocessing technologies in reverse engineering. Most simplification algorithms always lose geometric feature excessively in the process. On the basis of feature extraction, a new algorithm is proposed for the simplification of scattered point cloud with unit normal vectors. First, points in point cloud are distributed into uniform cubes. Next, bounding spheres are constructed with their centers at each point; accordingly K-nearest neighbors are searched in the relevant sphere. Later, a specified function is defined to measure the curvature of each point so that feature points can be extracted. Finally, feature points and non-feature points are simplified according to the radius of bounding sphere and the threshold of normal vectors’ inner product. The experiments show that the proposed algorithm has the advantages of fast speed and high reservation of the geometric feature of point cloud.",
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"content": "Simplification of scattered point cloud is one of the key preprocessing technologies in reverse engineering. Most simplification algorithms always lose geometric feature excessively in the process. On the basis of feature extraction, a new algorithm is proposed for the simplification of scattered point cloud with unit normal vectors. First, points in point cloud are distributed into uniform cubes. Next, bounding spheres are constructed with their centers at each point; accordingly K-nearest neighbors are searched in the relevant sphere. Later, a specified function is defined to measure the curvature of each point so that feature points can be extracted. Finally, feature points and non-feature points are simplified according to the radius of bounding sphere and the threshold of normal vectors’ inner product. The experiments show that the proposed algorithm has the advantages of fast speed and high reservation of the geometric feature of point cloud.",
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"abstract": "This paper presents a theoretical lens for research on social media use in eParticipation, along with an example case study. The idea of the public sphere and how it can be applied to eParticipation research is presented. The public sphere is discussed in relation to Cast ell's notion of the network society as the \"networked public sphere\", and social capital is introduced as a possible explanation for why some people choose to participate while others refrain from doing so. An example case is presented and analysed in terms of the public sphere and social capital. Finally, the argument is made that working public spheres, enacted through various online social media platforms, can contribute to increased social capital and increased political debate among citizens.",
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"content": "This paper presents a theoretical lens for research on social media use in eParticipation, along with an example case study. The idea of the public sphere and how it can be applied to eParticipation research is presented. The public sphere is discussed in relation to Cast ell's notion of the network society as the \"networked public sphere\", and social capital is introduced as a possible explanation for why some people choose to participate while others refrain from doing so. An example case is presented and analysed in terms of the public sphere and social capital. Finally, the argument is made that working public spheres, enacted through various online social media platforms, can contribute to increased social capital and increased political debate among citizens.",
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"abstract": "3-D surface reconstruction based on contour maps is a current research focus. However, to read contour maps requires some professional knowledge and to reconstruct 3-D surface with contour maps needs professional tools such as OpenGL and high programming capabilities. This paper presents an algorithm of 3-D surface reconstruction based on scattered points. First, the algorithm triangulates with these scattered points in plane and segments each triangle to get some interior points, then calculates the coordinate of each point as well as its elevation. Last, the altorithm triangulates with all the points and generates triangular patches in 3-D space. The experiment result and complexity analysis of the algorithm show that the algorithm is simple and easy to achieve expected results.",
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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 a-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": "This work proposes a novel framework for optimization in the constrained diffeomorphism space for deformable surface registration. First the diffeomorphism space is modeled as a special complex functional space on the source surface, the Beltrami coefficient space. The physically plausible constraints, in terms of feature landmarks and deformation types, define subspaces in the Beltrami coefficient space. Then the harmonic energy of the registration is minimized in the constrained subspaces. The minimization is achieved by alternating two steps: 1) optimization - diffuse the Beltrami coefficient, and 2) projection - first deform the conformal structure by the current Beltrami coefficient and then compose with a harmonic map from the deformed conformal structure to the target. The registration result is diffeomorphic, satisfies the physical landmark and deformation constraints, and minimizes the conformality distortion. Experiments on human facial surfaces demonstrate the efficiency and efficacy of the proposed registration framework.",
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