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A proof-of-concept study for predicting the region of atherosclerotic plaque development based on plaque growth modeling in carotid arteries

A proof-of-concept study for predicting the region of athero...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Antonis I. Sakellarios Paschalis Bizopoulos Kostas Stefanou Lambros S. Athanasiou Michail I. Papafaklis Christos V. Bourantas Katerina K. Naka Lampros K. Michalis Dimitrios I. Fotiadis the Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering University of Ioannina GR 45110 the Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens (NTUA) GR-157 73 Zografou Athens Greece the Institute of Molecular Biology and Biotechnology Dept. of Biomedical Research FORTH GR45110 Ioannina Greece the Michaelideion Cardiac Center Dept. of Cardiology in Medical School University of Ioannina GR 45110 Ioannina Greece the Dept. of Interventional Cardiology Erasmus MC Thoraxcenter Rotterdam The Netherlands the Biomedical Research Institute - FORTH University Campus of Ioannina 45110 Ioannina Greece

ISBN: (纸本)9781424492695

In this work, we present a computational model for plaque growth utilizing magnetic resonance data of a patient's carotid artery. More specifically, we model blood flow utilizing the Navier-Stokes equations, as well as LDL and HDL transport using the convection-diffusion equation in the arterial lumen. The accumulated LDL in the arterial wall is oxidized considering the protective effect of HDL. Macrophages recruitment and foam cells formation are the final step of the proposed multi-level modeling approach of the plaque growth. The simulated results of our model are compared with the follow-up MRI findings in 12 months regarding the change to the arterial wall thickness. WSS and LDL may indicate potential regions of plaque growth (R~2=0.35), but the contribution of foam cells formation, macrophages and oxidized LDL increased the prediction significantly (R~2=0.75).

关键词： LDL HDL MRI Low-Density Lipoproteins Hardware design languages High-density lipoproteins Carotid Arteries atherosclerotic plaque convection-diffusion equation Foam Cells C7orf49 gene Magnetic Resonance Imaging oxidized low density lipoprotein Blood Flow Blood Circulation Arteriosclerosis Computational modeling

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Multi-graph learning with positive and unlabeled bags 14

Multi-graph learning with positive and unlabeled bags

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14th SIAM International Conference on Data Mining, SDM 2014

作者： Wu, Jia Hong, Zhibin Pan, Shirui Zhu, Xingquan Zhang, Chengqi Cai, Zhihua Centre for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Australia Dept. of Computer and Electrical Engineering and Computer Science Florida Atlantic University United States Dept. of Computer Science China University of Geosciences Wuhan430073 China

ISBN: (纸本)9781510811515

In this paper, we formulate a new multi-graph learning task with only positive and unlabeled bags, where labels are only available for bags but not for individual graphs inside the bag. This problem setting raises significant challenges because bag-of-graph setting does not have features to directly represent graph data, and no negative bags exits for deriving discriminative classification models. To solve the challenge, we propose a puMGL learning framework which relies on two iteratively combined processes for multigraph learning: (1) deriving features to represent graphs for learning;and (2) deriving discriminative models with only positive and unlabeled graph bags. For the former, we derive a subgraph scoring criterion to select a set of informative subgraphs to convert each graph into a feature space. To handle unlabeled bags, we assign a weight value to each bag and use the adjusted weight values to select most promising unlabeled bags as negative bags. A margin graph pool (MGP), which contains some representative graphs from positive bags and identified negative bags, is used for selecting subgraphs and training graph classifiers. The iterative subgraph scoring, bag weight updating, and MGP based graph classification forms a closed loop to find optimal subgraphs and most suitable unlabeled bags for multi-graph learning. Experiments and comparisons on real-world multigraph data demonstrate the algorithm performance. Copyright © SIAM.

关键词： Directed graphs

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Mid-IR Filamentation in Dielectrics: 3-octave-spanning Supercontinuum Generation and Sub-2-cycle Self-compression

Mid-IR Filamentation in Dielectrics: 3-octave-spanning Super...

引用

Conference on Lasers and Electro-Optics

作者： Houkun Liang Peter Krogen Ross Grynko Ondrej Novak Chun-Lin Chang Gregory J. Stein Darshana Weerawarne Bonggu Shim Franz X. Kartner Kyung-Han Hong Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge Massachusetts USA Dept. of Physics Applied Physics & Astronomy State University of New York Binghamton New York USA The Hamburg Center for Ultrafast Imaging Hamburg Germany DESY University of Hamburg Hamburg Germany

We report on 3-octave supercontinuum generation from ZnS in the normal dispersion regime and self-compression of mid-IR pulses to sub-2-cycle in CaF_2 in the anomalous dispersion regime. Temporal characterization show... 详细信息

关键词： II-VI semiconductor materials Zinc compounds Supercontinuum generation Optical fiber amplifiers Dielectrics Dispersion

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Public-key authentication for cloud-based WBANs 9

Public-key authentication for cloud-based WBANs

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9th International Conference on Body Area Networks, BODYNETS 2014

作者： Hayajneh, Thaier Vasilakos, Athanasios V. Almashaqbeh, Ghada Mohd, Bassam J. Imran, Muhammad A. Shakir, Muhammad Z. Qaraqe, Khalid A. School of Engineering and Computing Sciences New York Institute of Technology New York United States Computer Science Department Kuwait University Kuwait Dept. of Computer Science and Engineering University of Notre Dame Notre DameIN United States Computer Engineering Dept. Hashemite University Zarqa Jordan Center for Communication Systems Research University of Surrey United Kingdom Dept. of Electrical and Computer Engineering Texas A and M University at Qatar Qatar

ISBN: (纸本)9781631900471

Merging WBAN systems with cloud computing is an efficient solution to overcome limitations inherent in WBAN, especially in critical human-related applications such as remote health monitoring. In cloud-based WBAN, the nodes are classified into WBAN sensors that report measurements about the human body and WBAN actuators that receive commands from the medical staff and perform actions. Authenticating these commands is a critical security issue as any alteration may lead to serious consequences. This paper presents a light-weight public-key authentication protocol for cloud-based WBAN systems. The proposed protocol is based on the modified Rabin authentication algorithm which is customized in this paper by making some of its components run in parallel. To prove the efficiency of the modified Rabin we implemented the algorithm with different hardware settings using Tmote Sky motes. The Rabin algorithm with and without the parallel settings is also programmed on FPGA to evaluate its design and performance. The results show that secure, direct, instant, and authenticated commands can be delivered from the medical staff located at the cloud side to the WBAN nodes located in/on the human body. Compared to other public-key protocols implemented on the motes, Rabin algorithm achieved extremely faster verification and reasonable signature gen- eration speed. Moreover, the suggested parallel settings of the Rabin signature generation significantly reduced the delays( by almost 80%) which is a critical issue in WBAN applications. Copyright © 2014 ICST.

关键词： Authentication

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FPGA-based biophysically-meaningful modeling of olivocerebellar neurons 14

FPGA-based biophysically-meaningful modeling of olivocerebel...

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2014 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, FPGA 2014

作者： Smaragdos, Georgios Isaza, Sebastian Van Eijk, Martijn Sourdis, Ioannis Strydis, Christos Dept. of Neuroscience Erasmus Medical Center Rotterdam Netherlands Dept. de Ingeniería Electrónica Universidad de Antioquia UdeA Medellín Colombia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (纸本)9781450326711

The Inferior-Olivary nucleus (ION) is a well-charted region of the brain, heavily associated with sensorimotor control of the body. It comprises ION cells with unique properties which facilitate sensory processing and motor-learning skills. Various simulation models of ION-cell networks have been written in an attempt to unravel their mysteries. However, simulations become rapidly intractable when biophysically plausible models and meaningful network sizes (100 cells) are modeled. To overcome this problem, in this work we port a highly detailed ION cell network model, originally coded in Matlab, onto an FPGA chip. It was first converted to ANSI C code and extensively profiled. It was, then, translated to HLS C code for the Xilinx Vivado toolflow and various algorithmic and arithmetic optimizations were applied. The design was implemented in a Virtex 7 (XC7VX485T) device and can simulate a 96-cell network at real-time speed, yielding a speedup of 700 compared to the original Matlab code and 12.5 compared to the reference C implementation running on a Intel Xeon 2.66GHz machine with 20GB RAM. For a 1,056-cell network (non-real-time), an FPGA speedup of 45 against the C code can be achieved, demonstrating the design's usefulness in accelerating neuroscience research. Limited by the available on-chip memory, the FPGA can maximally support a 14,400-cell network (non-real-time) with online parameter configurability for cell state and network size. The maximum throughput of the FPGA IONnetwork accelerator can reach 2.13 GFLOPS.

关键词： Neural networks

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New marker for real-time industrial robot programming by motion imitation

New marker for real-time industrial robot programming by mot...

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2014 IEEE International Conference on Robotics and Automation, ICRA 2014

作者： Ferreira, Marcos Costa, Paulo Rocha, Luis Moreira, A. Paulo Pires, Norberto Dept. of Electrical and Computer Eng. Faculty of Eng. Univ. of Porto Portugal INESC TEC -Institute for Systems and Computer Engineering of Porto Portugal Dept. of Mechanical Eng. Faculty of Science and Technology Univ. of Coimbra Portugal

This paper presents a new marker for robot programming by demonstration through motion imitation. The device is based on high intensity LEDs (light emission diodes) which are captured by a pair of industrial cameras. Using stereoscopy, the marker supplies 6-DoF (degrees of freedom) human wrist tracking with both position and orientation data. We propose a robust technique for camera and stereo calibration which maps camera coordinates directly into the desired robot frame, using a single LED. The calibration and tracking procedures are thoroughly described. The tests show that the marker presents a new robust, accurate and intuitive method for industrial robot programming. The system is able to perform in real-time and requires only a single pair of industrial cameras though more can be used for improved effectiveness and accuracy. © 2014 IEEE.

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Multi-instance learning from positive and unlabeled bags

Multi-instance learning from positive and unlabeled bags

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18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014

作者： Wu, Jia Zhu, Xingquan Zhang, Chengqi Cai, Zhihua Centre for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney NSW 2007 Australia Dept. of Computer and Electrical Engineering and Computer Science Florida Atlantic University Boca Raton FL 33431 United States Dept. of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9783319066073

Many methods exist to solve multi-instance learning by using different mechanisms, but all these methods require that both positive and negative bags are provided for learning. In reality, applications may only have positive samples to describe users' learning interests and remaining samples are unlabeled (which may be positive, negative, or irrelevant to the underlying learning task). In this paper, we formulate this problem as positive and unlabeled multi-instance learning (puMIL). The main challenge of puMIL is to accurately identify negative bags for training discriminative classification models. To solve the challenge, we assign a weight value to each bag, and use an Artificial Immune System based self-adaptive process to select most reliable negative bags in each iteration. For each bag, a most positive instance (for a positive bag) or a least negative instance (for an identified negative bag) is selected to form a positive margin pool (PMP). A weighted kernel function is used to calculate pairwise distances between instances in the PMP, with the distance matrix being used to learn a support vector machines classifier. A test bag is classified as positive if one or multiple instances inside the bag are classified as positive, and negative otherwise. Experiments on real-world data demonstrate the algorithm performance. © 2014 Springer International Publishing.

关键词： Iterative methods

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Super-graph classification

Super-graph classification

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18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014

作者： Guo, Ting Zhu, Xingquan Centre for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney NSW 2007 Australia Dept. of Computer and Electrical Engineering and Computer Science Florida Atlantic University Boca Raton FL 33431 United States

ISBN: (纸本)9783319066073

Graphs are popularly used to represent objects with dependency structures, yet all existing graph classification algorithms can only handle simple graphs where each node is a single attribute (or a set of independent attributes). In this paper, we formulate a new super-graph classification task where each node of the super-graph may contain a graph (a single-attribute graph), so a super-graph contains a set of inter-connected graphs. To support super-graph classification, we propose a Weighted Random Walk Kernel (WRWK) which generates a product graph between any two super-graphs, and uses the similarity (kernel value) of two single-attribute graph as the node weight. Then we calculate weighted random walks on the product graph to generate kernel value between two super-graphs as their similarity. Our method enjoys sound theoretical properties, including bounded similarity. Experiments confirm that our method significantly outperforms baseline approaches. © 2014 Springer International Publishing.

关键词： Graph theory

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Global and local Gram-Schmidt methods for hyperspectral pansharpening

Global and local Gram-Schmidt methods for hyperspectral pans...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Mauro Dalla Mura Gemine Vivone Rocco Restaino Paolo Addesso Jocelyn Chanussot Institut Polytechnique de Grenoble Grenoble RhÃ´ne-Alpes FR NATO STO Center for Maritime Research and Experimentation La Spezia Italy Dept. of Information Eng. Electrical Eng. and Applied Math. University of Salerno Salerno Italy Faculty of Electrical and Computer Engineering University of Iceland Reykjavik Iceland GIPSA-Lab Grenoble Institute of Technology Grenoble France

Pansharpening algorithms enable to produce synthetic data with high spatial details and spectral diversity by combining a panchromatic image with multispectral or hyperspectral data. In classical approaches the details extracted from the panchromatic image are introduced into the original multichannel image through injection gains, which can be spatially variant on the image. In this paper we analyze several methods for partitioning an image into regions in which the pixels will share the same injection coefficients. Gram-Schmidt pansharpening methods are used as paradigmatic examples for assessing the performance of global and local gain estimation strategies, using hyperspectral data acquired by sensors mounted on one (Earth Observing-1) or multiple (PROBA and Quick-bird) satellite platforms.

关键词： Hyperspectral imaging Sensors Spatial resolution Indexes Image segmentation

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Accelerating restore and garbage collection in deduplication-based backup systems via exploiting historical information

Accelerating restore and garbage collection in deduplication...

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2014 USENIX Annual Technical Conference, USENIX ATC 2014

作者： Fu, Min Feng, Dan Hua, Yu He, Xubin Chen, Zuoning Xia, Wen Huang, Fangting Liu, Qing Wuhan National Lab for Optoelectronics China School of Computer Huazhong University of Science and Technology Wuhan China Dept. of Electrical and Computer Engineering Virginia Commonwealth University VA United States National Engineering Research Center for Parallel Computer Beijing China

ISBN: (纸本)9781931971102

In deduplication-based backup systems, the chunks of each backup are physically scattered after deduplication, which causes a challenging fragmentation problem. The fragmentation decreases restore performance, and results in invalid chunks becoming physically scattered in different containers after users delete backups. Existing solutions attempt to rewrite duplicate but fragmented chunks to improve the restore performance, and reclaim invalid chunks by identifying and merging valid but fragmented chunks into new containers. However, they cannot accurately identify fragmented chunks due to their limited rewrite buffer. Moreover, the identification of valid chunks is cumbersome and the merging operation is the most time-consuming phase in garbage collection. Our key observation that fragmented chunks remain fragmented in subsequent backups motivates us to propose a History-Aware Rewriting algorithm (HAR). HAR exploits historical information of backup systems to more accurately identify and rewrite fragmented chunks. Since the valid chunks are aggregated in compact containers by HAR, the merging operation is no longer required. To reduce the metadata overhead of the garbage collection, we further propose a Container-Marker Algorithm (CMA) to identify valid containers instead of valid chunks. Our extensive experimental results from real-world datasets show HAR significantly improves the restore performance by 2.6X-17X at a cost of only rewriting 0.45-1.99% data. CMA reduces the metadata overhead for the garbage collection by about 90X. © Proceedings of the 2014 USENIX Annual Technical Conference, USENIX ATC 2014. All rights reserved.

关键词： Containers

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