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2007 1st International Conference on Bioinformatics and Biomedical engineering, ICBBE

作者： Wang, Yanni Zheng, Yao Xie, Lijun Zhou, Dibin Luan, Cuiju College of Computer Science Center for Engineering and Scientific Computation Zhejiang University Hangzhou China College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)1424411203

For 3D medical data visualization, typically Transfer Functions (TFs) are employed to classify the data and to assign visual attributes to each material class. However, it is generally difficult to obtain a good transfer function. This paper presents a novel approach for exploring appropriate transfer functions by utilizing Artificial Intelligent (AI) techniques: the search for a transfer function is reformulated as a global optimization problem, which is subsequently solved by using the Particle Swarm Optimizers (PSO). The initial population of transfer functions is pre-defined by users or selected randomly. The fitness value of each transfer function is defined by user on associated final rendering images or by user-defined objective functions. This approach bridges transfer functions and rendering images, and offers users an image that focuses on the part they interested in the medical datasets. © 2007 IEEE.

关键词： Medical computing

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Incorporating prior knowledge on features into learning

Incorporating prior knowledge on features into learning

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11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007

作者： Krupka, Eyal Tishby, Naftali School of Computer Science and Engineering Interdisciplinary Center for Neural Computation Hebrew University Jerusalem 91904 Israel Intel Research Israel Israel

In the standard formulation of supervised learning the input is represented as a vector of features. However, in most real-life problems, we also have additional information about each of the features. This information can be represented as a set of properties, referred to as meta-features. For instance, in an image recognition task, where the features are pixels, the meta-features can be the (x, y) position of each pixel. We propose a new learning framework that incorporates meta-features. In this framework we assume that a weight is assigned to each feature, as in linear discrimination, and we use the meta-features to define a prior on the weights. This prior is based on a Gaussian process and the weights are assumed to be a smooth function of the meta-features. Using this framework we derive a practical algorithm that improves generalization by using meta-features and discuss the theoretical advantages of incorporating them into the learning. We apply our framework to design a new kernel for handwritten digit recognition. We obtain higher accuracy with lower computational complexity in the primal representation. Finally, we discuss the applicability of this framework to biological neural networks.

关键词： Pixels

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Uncovering shared structures in multiclass classification 07

Uncovering shared structures in multiclass classification

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24th International Conference on Machine Learning, ICML 2007

作者： Amit, Yonatan Fink, Michael Srebro, Nathan Ullman, Shimon School of Computer Science and Engineering Hebrew University Israel Center for Neural Computation Hebrew University Israel Toyota Technological Institute Chicago United States Weizmann Institude of Science Israel

ISBN: (纸本)9781595937933

This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using trace-norm regularization and study gradient-based optimization both for the linear case and the kernelized setting.

关键词： Learning systems

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Predicting secret keys via branch prediction

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Cryptographers Track at the RSA Conference, CT-RSA 2007

作者： Acıiçmez, Onur Koç, Çetin Kaya Seifert, Jean-Pierre School of Electrical Engineering and Computer Science Oregon State University CorvallisOR97331 United States Information Security Research Center Istanbul Commerce University Eminönü Istanbul34112 Turkey Applied Security Research Group The Center for Computational Mathematics and Scientific Computation Faculty of Science and Science Education University of Haifa Haifa31905 Israel Institute for Computer Science University of Innsbruck Innsbruck6020 Austria

ISBN: (纸本)9783540693277

This paper announces a new software side-channel attack — enabled by the branch prediction capability common to all modern high-performance CPUs. The penalty paid (extra clock cycles) for a mispredicted branch can be used for cryptanalysis of cryptographic primitives that employ a data-dependent program flow. Analogous to the recently described cache-based side-channel attacks our attacks also allow an unprivileged process to attack other processes running in parallel on the same processor, despite sophisticated partitioning methods such as memory protection, sandboxing or even virtualization. In this paper, we will discuss several such attacks for the example of RSA, and experimentally show their applicability to real systems, such as OpenSSL and Linux. Moreover, we will also demonstrate the strength of the branch prediction side-channel attack by rendering the obvious countermeasure in this context (Montgomery Multiplication with dummy-reduction) as useless. Although the deeper consequences of the latter result make the task of writing an efficient and secure modular exponentiation (or scalar multiplication on an elliptic curve) a challenging task, we will eventually suggest some countermeasures to mitigate branch prediction side-channel attacks. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Side channel attack

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A Novel Pointcloud-based Isosurface Extraction

A Novel Pointcloud-based Isosurface Extraction

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10th IEEE International Conference on computer Aided Design and computer Graphics(第十届CAO/Graphics国际会议)

作者： Dibin Zhou Kangjian Wang Lijun Xie Yao Zheng Yanni Wang College of Computer Science and Center for Engineering and Scientific Computation Zhejiang UniversityHangzhouZhejiang310027China

In this paper,we present a novel approach of hardware-accelerated pointcloud-based isosurface extraction on tetrahedral cells. In contrast to previous methods, our method takes advantage of programmable capability on the modern graphics hardware to render the isosurface silhouette and reduces the video memory consumption per tetrahedron. We achieve this by using a GPU-based geometry-generating method called fat-point *** classify tetrahedra into three isosurface cases and compute corresponding parameters in vertex processors,and then generate implicit isosurface silhouette in fragment processors. Utilizing the high performance OpenGL vertex buffer objects, our algorithm can achieve a rendering rate of three million tetrahedra per second.

关键词： Isosurfaces Hardware Data mining Data visualization Rendering (computer graphics) Medical simulation Educational institutions computer science computer graphics Sea surface

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Protein structural class prediction by combining conditional probability with information content

Protein structural class prediction by combining conditional...

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2nd International Multi-Symposiums on computer and computational sciences 2007, IMSCCS'07

作者： Fei, Gu Jun, Ni Hang, Chen Zhengge, Huang Department of Biotechnology College of Life Sciences Zhejiang University Hangzhou 310027 China Department of Computer Science University of Iowa Iowa City IA 52242 United States Department of Biomedical Engineering College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou 310027 China Department of Computer Science Center for Engineering and Scientific Computation Zhejiang University Hangzhou 310027 China

ISBN: (纸本)0769530397

Protein structural class is the most basic and important classification of protein structures. The prediction technique of protein structural class has been developing for decades. Two popular methods, the amino acid frequency based methods and amino acid arrangement and long-term correlation based methods, were proposed in previous work. However, there are opposite advantages and disadvantages in these methods. The former concentrated on statistical analysis while the latter emphasized long-term and biologically significant research. In this paper, we proposed a new method combining probability and information theory with long-term correlation consideration. The results showed that advantages of both original methods were included in our method and the accuracy is much higher than in the traditional procedures. © 2007 IEEE.

关键词： Proteins

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Information bottleneck for non co-occurrence data 19

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20th Annual Conference on Neural Information Processing Systems, NIPS 2006

作者： Seldin, Yevgeny Slonim, Noam Tishby, Naftali School of Computer Science and Engineering Hebrew University of Jerusalem Israel Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Israel Lewis-Sigler Institute for Integrative Genomics Princeton University United States

ISBN: (纸本)9780262195683

We present a general model-independent approach to the analysis of data in cases when these data do not appear in the form of co-occurrence of two variables X, Y, but rather as a sample of values of an unknown (stochastic) function Z(X, Y ). For example, in gene expression data, the expression level Z is a function of gene X and condition Y;or in movie ratings data the rating Z is a function of viewer X and movie Y. The approach represents a consistent extension of the Information Bottleneck method that has previously relied on the availability of co-occurrence statistics. By altering the relevance variable we eliminate the need in the sample of joint distribution of all input variables. This new formulation also enables simple MDL-like model complexity control and prediction of missing values of Z. The approach is analyzed and shown to be on a par with the best known clustering algorithms for a wide range of domains. For the prediction of missing values (collaborative filtering) it improves the currently best known results.

关键词： Collaborative filtering

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On approximation of inverse problem for abstract parabolic differential equations in Banach spaces

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Journal of Inverse and Ill-Posed Problems 2007年第8期15卷 831-851页

作者： Prilepko, A. Piskarev, S. Shaw, S.-Y. Department of Mathematical Analysis Moscow State University Vorobjevy Gory 119899 Moscow Russia Scientific Research Computer Center Moscow State University Vorobjevy Gory 119899 Moscow Russia Department of Mathematics Graduate School of Engineering Lunghwa University of Science and Technology Guishan Taoyuan 333 Taiwan

The paper is devoted to the approximation of a control element for an abstract parabolic equation. The presentation is given for general approximation schemes, which includes finite element methods, finite difference schemes, projection methods. We apply an iteration-approximation method for the investigation of inverse problems for parabolic equations. © de Gruyter 2007.

关键词： Abstract differential equations Co-semigroups Banach spaces discretization methods difference schemes discrete semigroups inverse problems Fredholm equations

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Intelligent Volume Visualization for Medical Datasets

Intelligent Volume Visualization for Medical Datasets

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The 1st International Conference on Bioinformatics and Biomedical engineering(iCBBE 2007)(首届IEEE生物信息与生物医学工程国际会议)

作者： Yanni Wang Lijun Xie Dibin Zhou Yao Zheng Cuiju Luan College of Computer Science and Center for Engineering and Scientific ComputationZhejiang Universi College of Information Engineering Shanghai Maritime UniversityShanghai P.R.China

关键词： Transfer Function Particle Swarm Optimization Medical dataset Classification

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Cluster stability for finite samples 07

Cluster stability for finite samples

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Proceedings of the 21st International Conference on Neural Information Processing Systems

作者： Ohad Shamir Naftali Tishby School of Computer Science and Engineering The Hebrew University Jerusalem Israel School of Computer Science and Engineering The Hebrew University Jerusalem Israel and Interdisciplinary Center for Neural Computation The Hebrew University Jerusalem Israel

ISBN: (纸本)9781605603520

Over the past few years, the notion of stability in data clustering has received growing attention as a cluster validation criterion in a sample-based framework. However, recent work has shown that as the sample size increases, any clustering model will usually become asymptotically stable. This led to the conclusion that stability is lacking as a theoretical and practical tool. The discrepancy between this conclusion and the success of stability in practice has remained an open question, which we attempt to address. Our theoretical approach is that stability, as used by cluster validation algorithms, is similar in certain respects to measures of generalization in a model-selection framework. In such cases, the model chosen governs the convergence rate of generalization bounds. By arguing that these rates are more important than the sample size, we are led to the prediction that stability-based cluster validation algorithms should not degrade with increasing sample size, despite the asymptotic universal stability. This prediction is substantiated by a theoretical analysis as well as some empirical results. We conclude that stability remains a meaningful cluster validation criterion over finite samples.

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