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检索条件"机构=Program in Applied Computer Science"

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Twice-Ramanujan sparsifiers

Twice-Ramanujan sparsifiers

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作者： Batson, Joshua Spielman, Daniel A. Srivastava, Nikhil Department of Mathematics MIT Cambridge MA 02139 United States Program in Applied Mathematics Department of Computer Science Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 United States

We prove that every graph has a spectral sparsifier with a number of edges linear in its number of vertices. As linear-sized spectral sparsifiers of complete graphs are expanders, our sparsifiers of arbitrary graphs can be viewed as generalizations of expander graphs. In particular, we prove that for every d > 1 and every undirected, weighted graph G = (V, E,w) on n vertices, there exists a weighted graph H = (V, F, w˜) with at most d(n - 1)] edges such that for every x ∈ v, xT LGx ≤ xT LHx ≤ (d+1+2√d/d+1+2√d)· x T Lgx, where LG and LH are the Laplacian matrices of G and H, respectively. Thus, H approximates G spectrally at least as well as a Ramanujan expander with dn/2 edges approximates the complete graph. We give an elementary deterministic polynomial time algorithm for constructing H. Copyright © by SIAM.

关键词： Polynomial approximation

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Instantaneous radar polarimetry with multiple dually-polarized antennas

Instantaneous radar polarimetry with multiple dually-polariz...

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40th Asilomar Conference on Signals, Systems, and computers, ACSSC '06

作者： Calderbank, A.R. Howard, S.D. Moran, W. Pezeshki, A. Zoltowski, M. Program in Applied and Computational Mathematics Princeton University Princeton NJ United States Defence Science and Technology Organization Edinburgh Australia Electrical Engineering and Computer Science University of Melbourne Melbourne Australia Electrical and Computer Engineering Purdue University West Lafayette IN United States

ISBN: (纸本)1424407850

Fully Polarimetric radar systems are capable of simultaneously transmitting and receiving in two orthogonal polarizations. Instantaneous radar polarimetry exploits both polarization modes of a dually-polarized radar transmitter and receiver on a pulse by pulse basis, and can improve the radar detection performance and suppress range sidelobes. In this paper, we extend the use of instantaneous radar polarimetry for radar systems with multiple dually-polarized transmit and receive antennas. Alamouti signal processing is used to coordinate transmission of Golay pairs of phase codes waveforms across polarizations and multiple antennas. The integration of multi-antenna signal processing with instantaneous radar polarimetry can further improve the detection performance, at a computational cost comparable to single channel matched filtering.

关键词： Polarimeters

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A Nearly-Linear Time Algorithm for Approximately Solving Linear Systems in a Symmetric M-Matrix

A Nearly-Linear Time Algorithm for Approximately Solving Lin...

引用

Combinatorial Scientific Computing 2009

作者： Daitch, Samuel I. Spielman, Daniel A. Yale University Department of Computer Science PO Box 208285 New HavenCT06520-8285 United States Yale University Program in Applied Mathematics and Department of Computer Science PO Box 208285 New HavenCT06520-8285 United States

We present an algorithm for solving a linear system in a symmetric M-matrix. In particular, for n × n symmetric M-matrix M, we show how to find a diagonal matrix D such that DMD is diagonally-dominant. To compute D, the algorithm must solve O (log n) linear systems in diagonally-dominant matrices. If we solve these diagonally-dominant systems approximately using the Spielman-Teng nearly-linear time solver [1], then we obtain an algorithm for approximately solving linear systems in symmetric M-matrices, for which the expected running time is also nearly-linear. © 2009 Combinatorial Scientific Computing. All Rights Reserved.

关键词： Linear systems

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Learning representation and control in continuous Markov decision processes

Learning representation and control in continuous Markov dec...

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21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06

作者： Mahadevan, Sridhar Maggioni, Mauro Ferguson, Kimberly Osentoski, Sarah Department of Computer Science University of Massachusetts 140 Governor's Drive Amherst MA 01003 Program in Applied Mathematics Department of Mathematics Yale University New Haven CT 06510

ISBN: (纸本)1577352815

This paper presents a novel framework for simultaneously learning representation and control in continuous Markov decision processes. Our approach builds on the framework of proto-value functions, in which the underlying representation or basis functions are automatically derived from a spectral analysis of the state space manifold. The proto-value functions correspond to the eigenfunctions of the graph Laplacian. We describe an approach to extend the eigenfunctions to novel states using the Nyström extension. A least-squares policy iteration method is used to learn the control policy, where the underlying subspace for approximating the value function is spanned by the learned proto-value functions. A detailed set of experiments is presented using classic benchmark tasks, including the inverted pendulum and the mountain car, showing the sensitivity in performance to various parameters, and including comparisons with a parametric radial basis function method. Copyright © 2006, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Knowledge representation

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Value function approximation with diffusion wavelets and Laplacian eigenfunctions

Value function approximation with diffusion wavelets and Lap...

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2005 Annual Conference on Neural Information Processing Systems, NIPS 2005

作者： Mahadevan, Sridhar Maggioni, Mauro Department of Computer Science University of Massachusetts Amherst MA 01003 United States Program in Applied Mathematics Department of Mathematics Yale University New Haven CT 06511 United States

ISBN: (纸本)9780262232531

We investigate the problem of automatically constructing efficient representations or basis functions for approximating value functions based on analyzing the structure and topology of the state space. In particular, two novel approaches to value function approximation are explored based on automatically constructing basis functions on state spaces that can be represented as graphs or manifolds: one approach uses the eigenfunctions of the Laplacian, in effect performing a global Fourier analysis on the graph;the second approach is based on diffusion wavelets, which generalize classical wavelets to graphs using multiscale dilations induced by powers of a diffusion operator or random walk on the graph. Together, these approaches form the foundation of a new generation of methods for solving large Markov decision processes, in which the underlying representation and policies are simultaneously learned.

关键词： Diffusion

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Stock Price Prediction and Recommendation Approach Based on Machine Learning 4

Stock Price Prediction and Recommendation Approach Based on ...

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4th IEEE Eurasia Conference on IoT, Communication and Engineering, ECICE 2022

作者： Chang, Chin-Chih Luo, Kei-Wen Hsiao, Sean Chung Hua University Bachelor's Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan

ISBN: (纸本)9781665482080

Investment is a lifetime work. Investing in stocks is a popular measure. However, investing in stocks is not easy, and losing money is not uncommon. We investigate if there is a systematic way to find buyable stocks and more importantly when to buy and sell them. Nowadays, with the progress of hardwarel, artificial intelligence (AI) has become a feasible solution to many issues. Machine learning and deep learning are two widely adopted methods in AI. In this study, we show how to apply machine learning to stock prediction and recommendation. After trials, we found that LightGBM shows its potential to predict and recommend stocks. We used the data of the Taiwan stock market to validate the method. We compared the prediction and recommendation with the wellknown Taiwan ETFs 0050 and 0056. The results show that the method is feasible, effective, efficient, and has better performance than Taiwan ETFs 0050 and 0056. © 2022 IEEE.

关键词： Deep learning

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Customer Service Chatbot Enhanced with Conversational Language Understanding and Knowledge Base 4

Customer Service Chatbot Enhanced with Conversational Langua...

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4th IEEE Eurasia Conference on IoT, Communication and Engineering, ECICE 2022

作者： Chang, Chin-Chih Cheng, Wen-Shing Hsiao, Sean Chung Hua University Bachelor's Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan

ISBN: (纸本)9781665482080

A customer service chatbot enhanced with conversational language understanding and knowledge base is developed. Here, we explore LUIS and QnA Maker which are unified as Azure cognitive service for language. LUIS is a cloudbased conversational AI service that responds a user intelligence. QnA maker is a knowledge base for custom question answering. This question-and-answer knowledge base is especially useful for customer dialogs. Hence, by combining the services, we provide a smart response and provide a knowledge base to understand and improve the service. We implement this chatbot on the LINE Bot platform. Users easily access it by simply adding the representative of this chatbot on LINE. In addition, we put marketing information through this chatbot. The experiments are conducted, and the results show the chatbot is feasible and has a high user acceptance. © 2022 IEEE.

关键词： Knowledge based systems

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Preconditioning techniques for reduced basis methods for parameterized elliptic partial differential equations

Preconditioning techniques for reduced basis methods for par...

引用

作者： Elman, Howard C. Forstall, Virginia Department of Computer Science Institute for Advanced Computer Studies University of Maryland College ParkMD20742 United States Applied Mathematics and Statistics and Scientific Computation Program University of Maryland College ParkMD20742 United States

The reduced basis methodology is an efficient approach to solve parameterized discrete partial differential equations when the solution is needed at many parameter values. An offline step approximates the solution space, and an online step utilizes this approximation, the reduced basis, to solve a smaller reduced problem, which provides an accurate estimate of the solution. Traditionally, the reduced problem is solved using direct methods. However, the size of the reduced system needed to produce solutions of a given accuracy depends on the characteristics of the problem, and it may happen that the size is significantly smaller than that of the original discrete problem but large enough to make direct solution costly. In this scenario, it may be more effective to use iterative methods to solve the reduced problem. We construct preconditioners for reduced iterative methods which are derived from preconditioners for the full problem. This approach permits reduced basis methods to be practical for larger bases than direct methods allow. We illustrate the effectiveness of iterative methods for solving reduced problems by considering two examples, the steady-state diffusion and convection-diffusion-reaction equations. © 2015 Society for Industrial and applied Mathematics.

关键词： Iterative methods

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Strategies of Collaborative Filtering Recommendation Mechanism Using a Deep Learning Approach 3

Strategies of Collaborative Filtering Recommendation Mechani...

引用

3rd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2021

作者： Chang, Chin-Chih Chang, Huan-Ming Chung Hua University Becholar Program in Applied Foreign Language and Smart Commerce Hsinchu30012 Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu30012 Taiwan

ISBN: (纸本)9781665445160

Nowadays, recommendation systems are widely used to help users locate the items they want. Collaborative filtering (CF) is a commonly used method for the recommendation. CF techniques use user-item ratings for prediction but suffer from the problems of data sparsity, cold start, and scalability. Though the Matrix Factorization (MF) techniques like Singular Value Decomposition (SVD) or Principal Component Analysis (PCA) overcome the above-mentioned problems, these methods are possible to deliver unmeaningful results in the condition of a low ranked approximation and denser singular vectors. In this paper, we review strategies of collaborative filtering recommendation mechanisms and propose an approach based on an autoencoder of convolutional neural network. Autoencoders are unsupervised learning methods in which neural networks are supported for the task of representation learning. We identify the user's features through learning, and then use these features to combine the collaborative filtering algorithm to recommend items. The experimental results show that the convolutional autoencoder can effectively reduce the computations when the amount of data is huge and benefited from the performance of its convolutional neural network. © 2021 IEEE.

关键词： Recommender systems

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Forces of Orthodontic Closed Coil Springs Measured using Fiber Bragg Gratings

Forces of Orthodontic Closed Coil Springs Measured using Fib...

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Latin America Optics and Photonics Conference, LAOP 2012

作者： Milczewski, Maura Scandelari Dinisio, Felipe Gonçalves Martelli, Cicero Borges, Paulo César Kalinowski, Hypolito José da Cunha, José Carlos Munhoz Federal University of Technology - Paraná Curitiba80230-901 Brazil Graduate Program on Electrical Engineering and Applied Computer Science Canada Department of Mechanical Engineering Brazilian Dentistry Association Paraná Brazil

The aim of this study is to compare forces generated by three different orthodontic closed coil springs supplied by three companies, optical fiber Bragg gratins are used to evaluate the force of closing of springs. &#... 详细信息

ISBN: (纸本)9781557529589

关键词： Fiber Bragg gratings

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