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A systematic review of structured sparse learning

Frontiers of Information Technology & Electronic Engineering 2017年第4期18卷 445-463页

作者： Lin-bo QIAO Bo-feng ZHANG Jin-shu SU Xi-cheng LU College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

High-dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumption of sparsity, many computational problems can be handled efficiently in practice. Structured sparse learning encodes the structural information of the variables and has been quite successful in numerous research fields. With various types of structures discovered, sorts of structured regularizations have been proposed. These regularizations have greatly improved the efficacy of sparse learning algorithms through the use of specific structural information. In this article, we present a systematic review of structured sparse learning including ideas, formulations, algorithms, and applications. We present these algorithms in the unified framework of minimizing the sum of loss and penalty functions, summarize publicly accessible software implementations, and compare the computational complexity of typical optimization methods to solve structured sparse learning problems. In experiments, we present applications in unsupervised learning, for structured signal recovery and hierarchical image reconstruction, and in supervised learning in the context of a novel graph-guided logistic regression.

关键词： Sparse learning Structured sparse learning Structured regularization

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A Hybrid Combination of Multiple SVM Classifiers for Automatic Recognition of the Damages and Symptoms on Plant Leaves 7th

A Hybrid Combination of Multiple SVM Classifiers for Automat...

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7th International Conference on image and Signal processing (ICISP)

作者： El Massi, Ismail Es-saady, Youssef El Yassa, Mostafa Mammass, Driss Benazoun, Abdeslam Ibn Zohr Univ IRF SIC Lab BP 80000 Agadir Morocco Inst Agron & Vet Hassan II Dept Plant Protect Complex Agadir Temara Morocco

ISBN: (纸本)9783319336183;9783319336176

A machine vision system is reported in this study for automatic recognition of the damages and symptoms on plant leaves from images. The system is based on a hybrid combination of three SVM classifiers including an individual classifier, which is used in parallel with a serial combination of two classifiers. The individual classifier adopts two types of features (texture and shape) to discriminate between the damages and symptoms. In serial architecture, the first classifier adopts the color features to classify the images;it considers the damages and/or symptoms that have a similar or nearest color belonging to the same class. Then, the second classifier is used to differentiate between the classes with similar color depending on the shape and texture features. A combination function is provided for comparing the decision of the individual classifier and of the serial architecture in order to achieve the final decision that represents the class of the form to be recognized. The tests of this study are carried out on six classes including three types of pest insects damages and three forms of fungal diseases symptoms. The results, with an overall recognition rate of 93.9%, show the advantages of the proposed method compared to the other existing methods.

关键词： Hybrid combination of classifiers Automatic recognition SVM Damages Symptoms Plant leaves image

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Evaluation of cartilage repair and osteoarthritis with sodium MRI

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NMR IN BIOMEDICINE 2016年第2期29卷 206-215页

作者： Zbyn, Stefan Mlynarik, Vladimir Juras, Vladimir Szomolanyi, Pavol Trattnig, Siegfried Med Univ Vienna Vienna Gen Hosp High Field MR Ctr Dept Biomed Imaging & Image Guided Therapy Vienna Austria CD Lab Clin Mol MR Imaging Vienna Austria Slovak Acad Sci Inst Measurement Sci Dept Imaging Methods Bratislava Slovakia

The growing need for early diagnosis and higher specificity than that which can be achieved with morphological MRI is a driving force in the application of methods capable of probing the biochemical composition of cartilage tissue, such as sodium imaging. Unlike morphological imaging, sodium MRI is sensitive to even small changes in cartilage glycosaminoglycan content, which plays a key role in cartilage homeostasis. Recent advances in high- and ultrahigh-field MR systems, gradient technology, phase-array radiofrequency coils, parallel imaging approaches, MRI acquisition strategies and post-processing developments have resulted in many clinical in vivo sodium MRI studies of cartilage, even at 3T. Sodium MRI has great promise as a non-invasive tool for cartilage evaluation. However, further hardware and software improvements are necessary to complete the translation of sodium MRI into a clinically feasible method for 3-T systems. This review is divided into three parts: (i) cartilage composition, pathology and treatment;(ii) sodium MRI;and (iii) clinical sodium MRI studies of cartilage with a focus on the evaluation of cartilage repair tissue and osteoarthritis. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： sodium MRI musculoskeletal cartilage repair osteoarthritis sodium concentration proteoglycans

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Universal steganography detector based on an artificial immune system for JPEG images 15

Universal steganography detector based on an artificial immu...

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Joint 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Symposium on parallel and distributed processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016

作者： Pérez, José De Jesús Serrano Rosales, Moises Salinas Cruz-Cortés, Nareli Cybersecurity Lab Centro de Investigación en Computación Instituto Politécnico Nacional Mexico City Mexico

ISBN: (纸本)9781509032051

Steganography is a hiding information technique heavily used nowadays. Though initially it was used to establish hidden communication channels, modern steganography has been found useful to hide code inside multimedia objects, mostly images. Its goal is to infiltrate malware into organizations or personal devices. This kind of malware is called stegomalware. As countermeasure, modern steganalysis methods employing different Computational Intelligence techniques such as Support Vector Machine, Machine Learning, Fisher Linear Discriminant, and others have been utilized. In this work we present a new stegananalysis method based on an Artificial Immune System (AIS), to detect JPEG images modified with three well known steganographic tools: F5, Outguess, or Steghide. It is also proposed the usage of Haar Wavelets to extract a feature vector that best describes the analyzed image, this is due the Haar Wavelets fast calculation and information synthesis. Our experimentation results are competitive against techniques representative of the state of the art. © 2016 IEEE.

关键词： image analysis

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SinglA image super-resolution via classified sparse Representation 13

SinglA image super-resolution via classified sparse Represen...

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13th International Conference on Embedded Software and System, ICESS 2016

作者： Lai, Chao Li, Fangzhao Li, Bao Jin, Shiyao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781509037261

Single image super-resolution reconstruction is a challenging ill-posed inverse problem currently. In this paper, we propose a method based on image classification and sparse representation for single image super-resolution reconstruction. Various images belong to different image category, and each category contains different contents and structures respectively, especially the high-frequency feature. Therefore, we extract the features of the input low-resolution image, and classify it into the corresponding category. Then the high-resolution image is reconstructed by sparse representation with the dictionary trained from the corresponding database, which consists of high-resolution and low-resolution image patch pairs. The experimental results demonstrate that our method achieves better performance in visual effect and qualitative analysis, by comparison with some well-known methods. © 2016 IEEE.

关键词： image classification

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Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets 4

Enabling Factor Analysis on Thousand-Subject Neuroimaging Da...

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4th IEEE International Conference on Big Data (Big Data)

作者： Anderson, Michael J. Capota, Mihai Turek, Javier S. Zhu, Xia Willke, Theodore L. Wang, Yida Chen, Po-Hsuan Manning, Jeremy R. Ramadge, Peter J. Norman, Kenneth A. Intel Corp Parallel Comp Lab Hillsboro OR 97124 USA Princeton Univ Princeton NJ 08544 USA Dartmouth Coll Hanover NH 03755 USA

ISBN: (纸本)9781467390057

The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and analyze the underlying brain activity. In this work, we consider two recent multi-subject factor analysis methods: the Shared Response Model and the Hierarchical Topographic Factor Analysis. We perform analytical, algorithmic, and code optimization to enable multi-node parallel implementations to scale. Single-node improvements result in 99x and 2062x speedups on the two methods, and enables the processing of larger datasets. Our distributed implementations show strong scaling of 3.3x and 5.5x respectively with 20 nodes on real datasets. We demonstrate weak scaling on a synthetic dataset with 1024 subjects, equivalent in size to the biggest fMRI dataset collected until now, on up to 1024 nodes and 32,768 cores.

关键词： functional Magnetic Resonance Imaging Multi-subject Analysis Scaling Factor Analysis

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A parallel and adaptive hybridized discontinuous Galerkin method for anisotropic nonhomogeneous diffusion

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COMPUTER methods IN APPLIED MECHANICS AND ENGINEERING 2016年 304卷 118-139页

作者： Samii, Ali Michoski, Craig Dawson, Clint Univ Texas Austin CHG ICES Austin TX 78712 USA Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA

The diffusion equation in anisotropic and nonhomogeneous media arises in the study of flow through porous media with sharp material interfaces. We discuss the solution of this problem by a hybrid discontinuous Galerkin (HDG) method. The method can be applied in three steps. First, we use a condensation technique to derive the scalar variable and the flux inside each element in terms of the numerical trace on the faces of that element. Then we form a global system of equations to solve for these numerical traces. We then solve the equation inside each element for the internal unknowns using the obtained numerical traces in the global solve step. Similar to other DG variants, HDG is a locally conservative method, and a noticeable share of calculations are performed independently within each element. In a mesh with pth order elements (p >= 0), this method gives p + 1 order of accuracy for smooth solutions for both the scalar variable and the flux. Moreover, by using a simple post-processing technique, one can reach an accuracy of order p + 2 for the scalar variable. To be able to handle problems with sharp material discontinuities, we use an adaptive refinement strategy, with octree grid structure. Hence, we avoid a larger global system which would arise from a fine uniform grid. In this process, we refine those elements with highest gradient of flux, at two sides of their faces. We have also utilized shared and distributed memory parallelism to enhance the performance of the method. The method is implemented using different modules of deal. ii (Bangerth and Kanschat, 1999), PETSc (Balay et al., 2015), p4est (Burstedde et al., 2011) and Hypre (Falgout and Yang, 2002). To demonstrate the accuracy, efficiency, scalability, and flexibility of the method, several two and three dimensional numerical experiments are studied. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Finite element method Hybrid methods Strong nonhomogeneity Anisotropy Porous media

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液晶光栅自干涉测量空间光调制器相位调制特性

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激光与光电子学进展 2017年第6期54卷 140-146页

作者：赵自新肖昭贤张航瑛赵宏西安交通大学机械工程学院机械制造系统国家重点实验室陕西西安710049

提出了一种测量平行液晶空间光调制器(SLM)相位调制特性的自干涉法。该方法实验简单,具有良好的稳健性。加载到SLM的灰度图由三部分组成,一部分是二元衍射光栅灰度图;另外两部分加载的灰度图均匀分布,下部分灰度值恒为0,上部分灰度值从... 详细信息

提出了一种测量平行液晶空间光调制器(SLM)相位调制特性的自干涉法。该方法实验简单,具有良好的稳健性。加载到SLM的灰度图由三部分组成,一部分是二元衍射光栅灰度图;另外两部分加载的灰度图均匀分布,下部分灰度值恒为0,上部分灰度值从0逐渐增加。随着上部分灰度的改变,经过相位调制的反射光束与光栅产生的+1级衍射光干涉生成一整幅错位条纹图,通过测量同一幅干涉条纹的周期和上下条纹之间的相对移动量来计算SLM的相位调制量大小,该方法消除了环境振动或空气湍流引起的条纹抖动对测量的影响,提高了测量精度。与其他方法相比,该方法获取SLM相位调制特性不需要复杂的光学装置,且具有优良的机械稳定性,反应快速更易实施。

关键词：测量液晶空间光调制器自干涉法相位调制特性图像后处理

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Tree-based coarsening and partitioning of complex networks

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ACM Journal of Experimental Algorithmics 2016年第2期21卷 1–20页

作者： Glantz, Roland Meyerhenke, Henning Schulz, Christian RG Parallel Computing Institute of Theoretical Informatics Karlsruhe Institute of Technology (KIT) Am Fasanengarten 5 Karlsruhe 76131 Germany

A hierarchy of increasingly coarse versions of a network allows one to represent the network on multiple scales at the same time. Often, the elementary operation for generating a hierarchy on a network is merging adjacent vertices, an operation that can be realized through contracting the edge between the two vertices. Such a hierarchy is defined by the selection of the edges to be contracted between a level and the next coarser level. The selection may involve (i) rating the edges, (ii) constraining the selection (e.g., that the selected edges form a matching), as well as (iii) maximizing the total rate of the selected edges under the constraints. Hierarchies of this kind are, among others, involved in multilevel methods for partitioning networks-a prerequisite for processing in parallel with distributed memory. In this article, we propose a new edge rating by (i) defining weights for the edges of a network that express the edges' importance for connectivity via shortest paths, (ii) computing a minimum weight spanning tree with respect to these weights, and (iii) rating the network edges based on the conductance values of the tree's fundamental cuts. To make the computation of our new edge rating efficient,we develop the first optimal linear-time algorithm to compute the conductance values of all fundamental cuts of a given spanning tree. We integrate the new edge rating into a leading multilevel graph partitioner and equip the latter also with a new greedy postprocessing for optimizing the Maximum Communication Volume (MCV) of a partition. Our experiments, in which we bipartition frequently used benchmark networks, show that the postprocessing reduces MCV by 11.3%. Our new edge rating, here used for matching-based coarsening, further reduces MCV by 10.3% compared to the previously best rating with MCV postprocessing in place for both ratings. In total, with a modest increase in running time, our new approach reduces the MCV of complex network partitions by 20.4%.

关键词： Complex networks Conductance Fundamental cuts Graph coarsening Multilevel graph partitioning Spanning trees

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A parallel microsimulation package for modelling cancer screening policies 12

A parallel microsimulation package for modelling cancer scre...

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12th IEEE International Conference on e-Science (e-Science)

作者： Karlsson, Andreas Olofsson, Niten Laure, Erwin Clements, Mark Karolinska Inst Dept Med Epidemiol & Biostat Stockholm Sweden SeRC Stockholm Sweden Karolinska Inst Dept Neurosci Stockholm Sweden Combitech AB Stockholm Sweden Royal Inst Technol Ctr High Performance Comp Stockholm Sweden

ISBN: (纸本)9781509042739

Microsimulation with stochastic life histories is an important tool in the development of public policies. In this article, we use microsimulation to evaluate policies for prostate cancer testing. We implemented the microsimulations as an R package, with pre- and post-processing in R and with the simulations written in C++. Calibrating a microsimulation model with a large population can be computationally expensive. To address this issue, we investigated four forms of parallelism: (i) shared memory parallelism using R;(ii) shared memory parallelism using OpenMP at the C++ level;(iii) distributed memory parallelism using R;and (iv) a hybrid shared/distributed memory parallelism using OpenMP at the C++ level and MPI at the R level. The close coupling between R and C++ offered advantages for ease of software dissemination and the use of high-level R parallelisation methods. However, this combination brought challenges when trying to use shared memory parallelism at the C++ level: the performance gained by hybrid OpenMP/MPI came at the cost of significant re-factoring of the existing code. As a case study, we implemented a prostate cancer model in the microsimulation package. We used this model to investigate whether prostate cancer testing with specific re-testing protocols would reduce harms and maintain any mortality benefit from prostate-specific antigen testing. We showed that four-yearly testing would have a comparable effectiveness and a marked decrease in costs compared with two-yearly testing and current testing. In summary, we developed a microsimulation package in R and assessed the cost-effectiveness of prostate cancer testing. We were able to scale up the microsimulations using a combination of R and C++, however care was required when using shared memory parallelism at the C++ level.

关键词： C++ languages Prostate cancer parallel processing Libraries Testing Computational modeling

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