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A Two-Stage Multi-loss Super-Resolution Network for Arterial Spin Labeling Magnetic Resonance Imaging 1

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10th international workshop on Machine Learning in Medical Imaging (MLMI) / 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Li, Zheng Liu, Qingping Li, Yiran Ge, Qiu Shang, Yuanqi Song, Donghui Wang, Ze Shi, Jun Shanghai Univ Sch Commun & Informat Engn Shanghai Inst Adv Commun & Data Sci Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai Peoples R China Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA Hangzhou Normal Univ Ctr Cognit & Brain Disorders Hangzhou Peoples R China Univ Maryland Sch Med Dept Diagnost Radiol & Nucl Med Baltimore MD 21201 USA

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) is a non-invasive technique for quantifying cerebral blood flow (CBF). Limited by the T1 decay rate of the labeled spins, very short time is available for data acquisition after one spin labeling cycle, resulting in a low spatial resolution. the traditional strategy to achieve high spatial resolution in ASL MRI is to add more labeling cycles. However, the total acquisition time is exponentially prolonged, making it highly sensitive to motions. Moreover, signal-to-noise-ratio (SNR) drops as spatial resolution increases. there needs an alternative approach to improve spatial resolution and SNR for ASL MRI without increasing scan time. therefore, we propose a novel two-stage multi-loss super-resolution (SR) network (TSMLSRNet) for reconstruction of high resolution ASL images. Specifically, the first stage network uses the mean squared error (MSE) loss function to produce a first SR estimate, while the second stage network adopts the gradient sensitive (GS) loss function to further improve high-frequency details for the output SR image. the multi-loss joint training strategy is finally used to preserve both the low-frequency and high-frequency information of the ASL images. Moreover, the noise in ASL images is simultaneously reduced. Validation results using in-vivo data clearly show the effectiveness of the proposed ASL SR algorithm that outperforms state-of-the-art image reconstruction algorithms.

关键词： Arterial spin labeling perfusion MRI Super-resolution Denoising Two-stage multi-loss joint training DenseNet

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Keynote abstract: Subspace search for community detection and community outlier mining in attributed graphs

Keynote abstract: Subspace search for community detection an...

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international workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Emmanuel Müller Knowledge Discovery and Data Mining Group Hasso-Plattner-Institute Germany

Attributed graphs are widely used for the representation of social networks, gene and protein interactions, communication networks, or product co-purchase in web stores. Each object is represented by its relationships to other objects (edge structure) and its individual properties (node attributes). For instance, social networks store friendship relations as edges and age, income, and other properties as attributes. these relationships and properties seem to be dependent on each other and exploiting these dependencies is beneficial, e.g. for community detection and community outlier mining. However, state-of-the-art techniques highly rely on this dependency assumption. In particular, community outlier mining is able to detect an outlier node if and only if connected nodes have similar values in all attributes. Such assumptions are generally known as homophily and are widely used. However, looking at multivariate spaces, one can observe that not all given attributes have high dependencies with the graph structure. For example, social properties such as income or age have strong dependencies with the graph structure of social networks. In contrast, properties such as gender are rather independent from it. Consequently, recent graph mining algorithms degenerate for multivariate attribute spaces that lack dependency with the graph structure in some of the attributes.

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Euclidean distance transform on a dedicated architecture based on a reconfigurable mesh network

Euclidean distance transform on a dedicated architecture bas...

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international Conference on Image Analysis and Processing

作者： P. Baglietto DIST-University of Genoa Genoa Italy

this paper describes a parallel algorithm for the Euclidean distance transform on a special-purpose architecture based on a reconfigurable mesh interconnection network. the proposed architecture, which supports the Euclidean distance transform algorithm as well as other low-level image processing algorithms, is particularly interesting because it can be effectively implemented in hardware and it can be programmed at a high level. the Euclidean distance transform algorithm described in this paper exploits the specific features of the reconfigurable interconnection network of the proposed dedicated architecture and takes advantage of the natural matching both between the data structure of the problem (a mesh of pixels) and that of the dedicated architecture (a mesh of processing elements) and between the nature of the computation (distance computation) and the capability of the interconnection network to let information flow from one node to a set of nodes by means of reconfigurable buses. the proposed algorithm has been implemented and has been validated through simulation, its computational complexity is O(N) (worst case) for pictures of N/spl times/N pixels on an architecture with N/spl times/N processing elements.

关键词： Euclidean distance Multiprocessor interconnection networks Computer architecture Computer networks data flow computing Parallel algorithms Image processing Hardware data structures Computational modeling

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Mariposa: a new architecture for distributed data

Mariposa: a new architecture for distributed data

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international Conference on data Engineering

作者： M. Stonebraker P.M. Aoki R. Devine W. Litwin M. Olson Div. of Comput. Sci. California Univ. Berkeley CA USA Computer Science Div. Department of EECS University of California Berkeley CA USA

We describe the design of Mariposa, an experimental distributed data management system that provides high performance in an environment of high data mobility and heterogeneous host capabilities. the Mariposa design unifies the approaches taken by distributed file systems and distributed databases. In addition, Mariposa provides a general, flexible platform for the development of new algorithms for distributed query optimization, storage management, and scalable data storage structures. this flexibility is primarily due to a unique rule-based design that permits autonomous, local-knowledge decisions to be made regarding data placement, query execution location, and storage management.< >

关键词： Distributed databases database systems File systems Computer architecture Computer networks Computer science Query processing Memory Protection Object oriented modeling

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P-EDR: An algorithm for parallel implementation of Parzen density estimation from uncertain observations

P-EDR: An algorithm for parallel implementation of Parzen de...

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international Symposium on Parallel Processing

作者： P.E. Lopez de Teruel J.M. Garcia M. Acacio O. Canovas Departamento de Ingeniería y Tecnología de Computadores Facultad de Informática University of Murcia Murcia Spain

We have developed a parallel version of a new algorithm for nonparametric density estimation when the input samples are not directly known, or they have some noise. the algorithm is an extension of the Parzen method for exact observations, but management of uncertainty implies heavy computational loads in terms of both calculus and storage. therefore, a parallel version of the algorithm is more adequate to solve this extended problem in a practical time, specially for samples of medium-large sizes. Our parallel algorithm has been designed in an SPMD style and implemented in a message-passing parallel environment. An efficient treatment of the distribution of the main data structure among processors, together with a low communication cost scheme results in a high scalability of the algorithm. Preliminary performance evaluations in a cluster of workstations show excellent speed-up results.

关键词： Uncertainty Calculus Parallel algorithms Algorithm design and analysis data structures Costs Scalability Clustering algorithms Workstations

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Parallel Saturation Based Model Checking

Parallel Saturation Based Model Checking

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international Symposium on Parallel and Distributed Computing

作者： Andr´s Voros Tam´s Szabo Attila J´mbor D´niel Darvas Akos Horv´th Tam´s Bartha Department of Measurement and Information Systems Budapest University of Technology and Economics Budapest Hungary Computer and Automation Research Institute MTA SZTAKI Budapest Hungary

Formal verification is becoming a fundamental step of safety-critical and model-based software development. As part of the verification process, model checking is one of the current advanced techniques to analyze the behavior of a system. In this paper, we examine an existing parallel model checking algorithm and we propose improvements to eliminate some computational bottlenecks. Our measurements show that the resulting new algorithm has better scalability and performance than both the former parallel approach and the sequential algorithm.

关键词： Synchronization Instruction sets data structures Computational modeling Algorithm design and analysis Firing Parallel algorithms

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Extending distributed shared memory to heterogeneous environments

Extending distributed shared memory to heterogeneous environ...

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international Conference on Distributed Computing Systems

作者： S. Zhou M. Stumm T. McInerney University of Toronto Toronto Canada

the problems of building a distributed shared memory system on a network of heterogeneous machines are discussed. An existing algorithm (due to K. Li, 1986) that implements distributed shared memory is extended to a heterogeneous environment. An implementation that runs on Sun and DEC Firefly multiprocessor workstations connected by Ethernet is described. Related implementation and performance issues are discussed. On the basis of measurements of the applications ported to the system, it is concluded that heterogeneous distributed shared memory is not only feasible but can also be compared in performance to its homogeneous counterpart.< >

关键词： Read-write memory Programming profession Message passing Partitioning algorithms Concurrent computing Sun Workstations Ethernet networks Access protocols data structures

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A New Fuzzy Belonging-based Multi-view K-means Clustering Algorithm

A New Fuzzy Belonging-based Multi-view K-means Clustering Al...

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international Conference and workshop on Computing and Communication (IEMCON)

作者： Junda Zhao Weiling Cai Yizhen Ge Lijuan Zhang Department of Computer Science University of Toronto Toronto Canada College of Computer Science and Technology Nanjing Normal University Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781728125312

Recently, a variety of data acquisition methods leads to the description of the same thing from different angles, which induces a large number of multi-view data. Clustering multi-view data in large-scale data analysis is of great significance and very challenging. In this paper, we present a new robust clustering algorithm, the Multi-view Fuzzy K-means clustering (MFKC) algorithm. In MFKC, a fuzzy membership method is used and optimized to describe the probability of a sample belonging to certain clusters. Compared with the hard partition based multi-view clustering algorithms, MFKC can effectively describe the fuzzy relationship between multi-view data and all clusters, and has better interpretability for the clustering results. In addition, MFKC also sets different weights for different views. By optimizing the multi-view clustering objective function, we obtain the optimal weigh of each view to reflect the importance of the view. the feasibility and effectiveness of the proposed algorithm is validated through various experimental comparisons.

关键词： Clustering algorithms Partitioning algorithms Time complexity data analysis Computer science Clustering methods Optimization

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An Index Scheme for XML Documents Based on Relationship Joins

An Index Scheme for XML Documents Based on Relationship Join...

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international Conference on Computer Supported Cooperative Work in Design

作者： Chengwen Wu Jinxiang Dong Gang Chen Lihua Yu College of Computer Science University of Zhejiang Hangzhou Zhejiang China

ISBN: (纸本)142440164X

XML is rapidly emerging as a standard for information storage, representation and exchange on the Web. How to rapidly search and query XML documents efficiently has received many attentions in resent research. However, current querying schemes of XML documents typically involve in both node content and tree structural information, which may limit efficiency when facing the application that the tree structural information is more complicated than the tree node itself. In this paper, we propose the node relationships joins algorithms that utilize available indexes mainly on tree structural information. the relationships join algorithms work perfectly especially for searching paths that are very long or whose lengths are unknown. Experimental results from our prototype system implementation highlight the correctness and efficiency of our solution

关键词： XML Collaborative work Navigation Tree data structures Educational institutions Computer science Prototypes database languages Communications technology Information representation

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Exploring Homogeneity and Covariance Matrix Structure of Multistatic/Polarimetric Sea-Clutter data

Exploring Homogeneity and Covariance Matrix Structure of Mul...

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IEEE Metrology for Aerospace

作者： V. Carotenuto A. Aubry A. De Maio F. Fioranelli DIETI University of Naples “Federico II” Naples Italy Department of Microelectronics MS3 Group Delft The Netherlands

the design of bespoke adaptive detection schemes relying on the joint use of multistatic/polarimetric measurements requires a preliminary statistical inference on the clutter interference environment. this is fundamental to develop an analytic model for the received signal samples, which is used to synthesize the radar detector. In this respect, the aim of this paper is the design of suitable learning tools to study some important statistical properties of the sea-clutter environment perceived at the nodes of a multistatic/polarimetric radar system. the study is complemented by the use of radar returns measured with the Netted RADar (NetRAD), which collects simultaneously monostatic and bistatic measurements. Precisely, the homogeneity properties of the data in the slow-time domain are first assessed resorting to Generalized Inner Product (GIP) based statistics. then, the possible presence of structures in the clutter covariance matrices (both inter and intra channels) is investigated through ad-hoc statistical tools. the results show that the data, regardless the polarimetric/geometric configuration, can be modeled as drawn from a stationary process within the coherence time. Moreover, for both the monostatic and the bistatic returns the structure of the covariance matrix depends upon the polarimetric/geometric configuration of the sensing system.

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