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7th World Congress on intelligent Control and Automation (WCICA 2008), vol.9

作者： Xiuquan Li Zhidong Deng State Key Laboratory of Intelligent Technology and Systems Department of Computer Science Tsinghua University Beijing China

A machine learning approach to predict turning points for chaotic time series was proposed through incorporating chaotic analysis into ensemble artificial neural network (ANN) modeling. The EM-like parameter learning algorithm for ensemble ANN model was presented. We then gave a new GA-based threshold optimization procedure using out-of-sample validation. The proposed approach was demonstrated on the benchmark chaotic time series like Mackey-Glass system. Our experimental results show significant improvement in performance over ANN model alone.

关键词： Ensemble neural network Chaotic time series Turning points prediction GA

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Exact quantum search by parallel unitary discrimination schemes

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Physical Review A 2008年第1期78卷 012303-012303页

作者： Xiaodi Wu Runyao Duan State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 China Department of Physics Tsinghua University Beijing 100084 China

We study the unsorted database search problem with items N from the viewpoint of unitary discrimination. Instead of considering the famous O(N) Grover bounded-error algorithm for the original problem, we seek the results for the exact algorithms, i.e., those that succeed with certainty. Under the standard oracle model ∑j(−1)δτj|j⟩⟨j|, we demonstrate a tight lower bound 23N+o(N) of the number of queries for any parallel scheme with unentangled input states. With the assistance of entanglement, we obtain a general lower bound 12(N−N). We provide concrete examples to illustrate our results. In particular, we show that the case of N=6 can be solved exactly with only two queries by using a bipartite entangled input state. Our results indicate that in the standard oracle model the complexity of the exact quantum search with one unique solution can be strictly less than that of the calculation of the OR function.

关键词： ALGORITHM

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Local distinguishability of orthogonal 2⊗3 pure states

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Physical Review A 2008年第1期77卷 012315-012315页

作者： Yu Xin Runyao Duan State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing 100084 China Department of Physics Tsinghua University Beijing 100084 China

We present a complete characterization for the local distinguishability of orthogonal 2⊗3 pure states except for some special cases of three states. Interestingly, we find there is a large class of four or three states that are indistinguishable by local projective measurements and classical communication (LPCC), but can be perfectly distinguished by LOCC. That indicates the ability of LOCC for discriminating 2⊗3 states is strictly more powerful than that of LPCC, which is strikingly different from the case of multiqubit states. We also show that classical communication plays a crucial role for local distinguishability by constructing a class of m⊗n states which require at least 2min{m,n}−2 rounds of classical communication in order to achieve a perfect local discrimination.

关键词： Ground state

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USING CHROMA HISTOGRAM TO MEASURE THE PERCEPTUAL SIMILARITY OF MUSIC

USING CHROMA HISTOGRAM TO MEASURE THE PERCEPTUAL SIMILARITY ...

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2008 IEEE International Conference on Multimedia and Expo (ICME 2008), vol.4

作者： Linxing Xiao Jie Zhou State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing China

Automatic evaluation of perceptual similarity is crucial for music retrieval. However, previous works mainly focused on the similarity of timbre and rhythm but not the musical pattern of a song, such as melody and chord. In this paper, we propose a new feature, chroma histogram, to summarize the musical pattern and use a transposition-invariant matching method to compare two chroma histograms. Experiment results demonstrate the efficiency of this method in measuring the similarity of musical pattern.

关键词： Music Acoustic signal analysis Signal representations Pattern matching

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Learning distance metric for semi-supervised image segmentation

Learning distance metric for semi-supervised image segmentat...

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IEEE International Conference on Image Processing

作者： Yangqing Jia Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing China

Semi-supervised image segmentation is an important issue in many image processing applications, and has been a popular research area recently, the most popular are graph-based methods. However, parameter selection in these methods is still largely heuristic. In this paper, we introduce distance metric learning into graph-based semi-supervised segmentation to automatically obtain good results for images with different appearances. We first derive the optimization problem with respect to the distance metric as well as the segmentation labels, and use gradient descent method to find a local optimum solution. Experiments on general images and the fungal disease analysis application have shown that our method provides a steady performance under casual user annotations and different image appearances.

关键词： Image segmentation Diseases Pixel Laplace equations Laboratories Automation Image processing Image analysis Crops Semisupervised learning

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Boosted Parametric Model for Human Detection

Boosted Parametric Model for Human Detection

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2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)

作者： Tongzhi Li Xiaoqing Ding Shengjin Wang State Key Laboratory of Intelligent Technology and Systems Dept.of Electronic Engineering Tsinghua University Beijing 10084

In this paper we discuss the issue of classifiers combined with Histogram of Oriented Gradients (HOG) descriptors for human detection. And we present a method that combines AdaBoost learning with HOG descriptors. The weak learners used in our algorithm are based on weighted Modified Quadratic Discriminant Functions (MQDF) which is a parametric model. We evaluate our algorithm on the INRIA person dataset. And the experimental results show that our approach achieves a comparable performance with the state of art methods both on accuracy and speed.

关键词： Classification algorithms Detectors Histograms Feature extraction Parametric statistics USA Councils Support vector machines

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Synchronization Enhancement of a Scale-free Highly Clustered Network

Synchronization Enhancement of a Scale-free Highly Clustered...

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7th World Congress on intelligent Control and Automation (WCICA 2008), vol.5

作者： Jianjun Niu Zhidong Deng State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

We investigate synchronization of a scale-free highly clustered echo state network with Hindmarsh-Rose equations. A new method, inspired by the connectivity between functional cortical columns, is proposed to improve the network in reservoir and enhance its synchrony. The experimental results show that synchronization can be achieved and kept stable with the increase of nodes. This modified complex network, characterized by nature-growing, preferential attachment, and synchrony, is critical to echo state network, and will help study the synchronization of real large-scale complex systems.

关键词： Phase synchronization Echo state network Complex network Hindmarsh-Rose equations

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A navigation system for minimally invasive abdominal intervention surgery robot

A navigation system for minimally invasive abdominal interve...

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IEEE Conference on Robotics, Automation and Mechatronics

作者： Weiming Zhai Yannan Zhao Peifa Jia State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

This paper aims to present a navigation system design for image guided minimal abdominal surgery robot that could compensate for the patient respiratory movement. Currently computer-aided surgery navigation technology has been broadly applied to such fields as orthopedics and neurosurgery, but in the field of interventional surgery, it is still rarely reported. As described in this paper, we introduced a surgery navigation system which can be applied to the interstitial treatment. Multiply technique are used to immobilizing the patient body, making the route plan and tracking the surgery instrument, real-time ultrasound feedback and image registration is also used to enhance the precision of instrument positioning. The goal of this research is to develop a computer-aided surgery navigation system in minimally invasive surgery, and provides solutions to urgent requirements in optical precision positioning, planning and navigation in abdominal surgeries. So that it can fine supporting completion of the surgery operation, improve accuracy and efficiency of the traditional surgical methods, and reduce the suffering of patients from pain.

关键词： Minimally invasive surgery Navigation Abdomen Robots Orthopedic surgery Surgical instruments Optical feedback Neurosurgery Ultrasonic imaging Image registration

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A Generative Probabilistic Model for Multi-label Classification

A Generative Probabilistic Model for Multi-label Classificat...

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IEEE International Conference on Data Mining (ICDM)

作者： Hongning Wang Minlie Huang Xiaoyan Zhu State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

Traditional discriminative classification method makes little attempt to reveal the probabilistic structure and the correlation within both input and output spaces. In the scenario of multi-label classification, most of the classifiers simply assume the predefined classes are independently distributed, which would definitely hinder the classification performance when there are intrinsic correlations between the classes. In this article, we propose a generative probabilistic model, the Correlated Labeling Model (CoL Model), to formulate the correlation between different classes. The CoL model is presented to capture the correlation between classes and the underlying structures via the latent random variables in a supervised manner. We develop a variational procedure to approximate the posterior distribution and employ the EM algorithm for the empirical Bayes parameter estimation. In our evaluations, the proposed model achieved promising results on various data sets.

关键词： Space technology Laboratories Text categorization Data mining intelligent systems intelligent structures Information science Computer science Labeling Random variables

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Semantic Concept Learning through Massive Internet Video Mining

Semantic Concept Learning through Massive Internet Video Min...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Peijiang Yuan Bo Zhang Jianmin Li State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

Semantic concept learning is one of the most challenging problems in video retrieval. The key barrier for semantic concept learning is lack of annotated training data. Internet videos are different from ordinary videos: massive, rich information, customized, non-uniform format, uneven quality, little descriptive text, only a few shots with limited length etc. Therefore, Internet is a potential repository to provide a reliable source for concept learning. In this paper, we focus on the semantic concept learning through known Internet video sources mining. Starting from the video-sharing websites, an automatical graph model generator for concepts relationship learning based on known ontology such as LSCOM, WordNet and ConceptNet is discussed. An automated source discovery method is addressed which prove to be useful in concept detection from the massive Internet videos. Experimental results prove that the addressed method is effective and efficient in semantic concept detection and learning through massive Internet video mining.

关键词： Internet Video sharing Video on demand Content based retrieval Learning systems Laboratories Information retrieval YouTube Data mining Conferences

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