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Conference on Optical Microlithography XXVIII

作者： Ma, Xu Gao, Jie Chen, Xuanbo Dong, Lisong Li, Yanqiu Beijing Inst Technol Sch Optoelect Minist Educ China Key Lab Photoelect Imaging Technol & Syst Beijing 100081 Peoples R China

ISBN: (纸本)9781628415285

Aerial image calculation is the basis of the current lithography simulation. As the critical dimension (CD) of the integrated circuits continuously shrinks, the thick mask near-field calculation has increasing influence on the accuracy and efficiency of the entire aerial image calculation process. This paper develops a flexible library-based approach to significantly improve the efficiency of the thick mask near-field calculation compared to the rigorous modeling method, while leading to much higher accuracy than the Kirchhoff approximation method. Specifically, a set of typical features on the fullchip are selected to serve as the training data, whose near-fields are pre-calculated and saved in the library. Given an arbitrary test mask, we first decompose it into convex corners, concave corners and edges, afterwards match each patch to the training layouts based on nonparametric kernel regression. Subsequently, we use the matched near-fields in the library to replace the mask patches, and rapidly synthesize the near-field for the entire test mask. Finally, a data-fitting method is proposed to improve the accuracy of the synthesized near-field based on least square estimate (LSE). We use a pair of two-dimensional mask patterns to test our method. Simulations show that the proposed method can significantly speed up the current FDTD method, and effectively improve the accuracy of the Kirchhoff approximation method.

关键词： Optical lithography thick mask near-field aerial image nonparametric kernel regression machine learning

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Performance evaluation of an acoustic indoor localization system based on a fingerprinting technique

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2014年第1期2014卷 1页

作者： Aloui, Nadia Raoof, Kosai Bouallegue, Ammar Letourneur, Stephane Zaibi, Sonia Univ Grenoble 3 Univ Grenoble 2 Univ Grenoble 1 Grenoble Inst TechnolCNRSUMR5216Grenoble Image Grenoble France Univ Tunis El Manar Ecole Natl Ingenieurs Tunis Lab Syst Commun LR 99 ES21 Tunis 1002 Tunisia Univ Maine LAUM Lab UMR CNRS 6613 F-72085 Le Mans France

We present an acoustic location system that adopts the time of arrival of the path of maximum amplitude as a signature and estimates the target position through nonparametric kernel regression. The system was evaluated in experiments for two main configurations: a privacy-oriented configuration with code division multiple access operation and a centralized configuration with time division multiple access operation. The effects of the number and positions of sources on the performance of the privacy-oriented system was studied. Moreover, the effect of the number of fingerprint positions on the performance of both systems was investigated. Results showed that our privacy-oriented scheme provides an accuracy of 8.5 cm with 87% precision, whereas our centralized system provides an accuracy of 2.7 cm for 93% of measurements. A comparison between our privacy-oriented system and another acoustic location system based on code division multiple access operation and lateration was conducted on our test bench and revealed that the cumulative error distribution function of the fingerprint-based system is better than that of the lateration-based system. This result is similar to that found for Wi-Fi radio-based localization. However, our experiments are the first to demonstrate the detrimental effect that reverberation has on naive acoustic localization approaches.

关键词： Acoustic Fingerprinting technique nonparametric kernel regression Code division multiple access operation Time division multiple access operation Privacy-oriented configuration Centralized system Lateration

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Uncertainty Estimation in Diffusion MRI Using the Nonlocal Bootstrap

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2014年第8期33卷 1627-1640页

作者： Yap, Pew-Thian An, Hongyu Chen, Yasheng Shen, Dinggang Univ N Carolina Dept Radiol Chapel Hill NC 27599 USA Univ N Carolina Biomed Res Imaging Ctr BRIC Chapel Hill NC 27599 USA Korea Univ Dept Brain & Cognit Engn Seoul 136713 South Korea

In this paper, we propose a new bootstrap scheme, called the nonlocal bootstrap (NLB) for uncertainty estimation. In contrast to the residual bootstrap, which relies on a data model, or the repetition bootstrap, which requires repeated signal measurements, NLB is not restricted by the data structure imposed by a data model and obviates the need for time-consuming multiple acquisitions. NLB hinges on the observation that local imaging information recurs in an image. This self-similarity implies that imaging information coming from spatially distant (nonlocal) regions can be exploited for more effective estimation of statistics of interest. Evaluations using in silico data indicate that NLB produces distribution estimates that are in closer agreement with those generated using Monte Carlo simulations, compared with the conventional residual bootstrap. Evaluations using in vivo data demonstrate that NLB produces results that are in agreement with our knowledge on white matter architecture.

关键词： Bootstrap diffusion magnetic resonance imaging (MRI) estimator nonlocal means nonparametric kernel regression sampling distribution self-similarity uncertainty

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Estimation of mean response via the effective balancing score

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BIOMETRIKA 2014年第3期101卷 613-624页

作者： Hu, Zonghui Follmann, Dean A. Wang, Naisyin NIAID Biostat Res Branch NIH Bethesda MD 20892 USA Univ Michigan Dept Stat Ann Arbor MI 48109 USA

We introduce the effective balancing score for estimation of the mean response under a missing-at-random mechanism. Unlike conventional balancing scores, the proposed score is constructed via dimension reduction free. of model specification. Three types of such scores are introduced, distinguished by whether they carry the covariate information about the missingness, the response, or both. The effective balancing score leads to consistent estimation with little or no loss in efficiency. Compared to existing estimators, it reduces the burden of model specification and is more robust. It is a near-automatic procedure which is most appealing when high-dimensional covariates are involved. We investigate its asymptotic and numerical properties, and illustrate its application with an HIV disease study.

关键词： Balancing score Dimension reduction Missingness at random nonparametric kernel regression Prognostic score Propensity score.

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Semiparametric Double Balancing Score Estimation for Incomplete Data With Ignorable Missingness

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2012年第497期107卷 247-257页

作者： Hu, Zonghui Follmann, Dean A. Qin, Jing NIAID Biostat Res Branch NIH Bethesda MD 20892 USA

When estimating the marginal mean response with missing observations, a critical issue is robustness to model misspecification. In this article, we propose a semiparametric estimation method with extended double robustness that attains the optimal efficiency under less stringent requirement for model specifications than the doubly robust estimators. In this semiparametric estimation, covariate information is collapsed into a two-dimensional score S. with one dimension for (i) the pattern of missingness and the other for (ii) the pattern of response, both estimated from some working parametric models. The mean response E(Y) is then estimated by the sample mean of E(Y vertical bar S), which is estimated via nonparametric regression. The semiparametric estimator is consistent if either the "core" of (i) or the "core" of (ii) is captured by S, and attains the optimal efficiency if both are captured by S. As the "cores" can be obtained without correctly specifying the full parametric models for (i) or (ii), the proposed estimator can be more robust than other doubly robust estimators. As S contains the propensity score as one component, the proposed estimator avoids the use and the shortcomings of inverse propensity weighting. This semiparametric estimator is most appealing for high-dimensional covariates, where fully correct model specification is challenging and nonparametric estimation is not feasible due to the problem of dimensionality. Numerical performance is investigated by simulation studies.

关键词： Doubly robust nonparametric kernel regression Propensity score

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Forecasting Performance of Nonlinear Models for Intraday Stock Returns

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JOURNAL OF FORECASTING 2012年第2期31卷 172-188页

作者： Matias, Jose M. Reboredo, Juan C. Univ Santiago de Compostela Dept Econ Santiago De Compostela 15782 Spain Univ Vigo Dept Stat ETSEM Vigo 36310 Spain

We studied the predictability of intraday stock market returns using both linear and nonlinear time series models. For the S&P 500 index we compared simple autoregressive and random walk linear models with a range of nonlinear models, including smooth transition, Markov switching, artificial neural network, nonparametric kernel regression and support vector machine models for horizons of 5, 10, 20, 30 and 60 minutes. The empirical results indicate that nonlinear models outperformed linear models on the basis of both statistical and economic criteria. Specifically, although return serial correlation receded by around 10 minutes, return predictability still persisted for up to 60?minutes according to nonlinear models, even though profitability decreases as time elapses. More flexible nonlinear models such as support vector machines and artificial neural network did not clearly outperform other nonlinear models. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： forecasting time series intraday stock returns stock price forecasting high-frequency data smooth transition regression Markov switching regression neural networks nonparametric kernel regression support vector machine regression

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A Novel Indoor Localization Scheme Based On Fingerprinting Technique And CDMA Signals

A Novel Indoor Localization Scheme Based On Fingerprinting T...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Aloui, Nadia Raoof, Kosai Bouallegue, Ammar Letourneur, Stephane Zaibi, Sonia Grenoble Univ GIPSA Lab Grenoble France ENIT Sch Com Lab 6 Tunis Tunisia Univ Maine CNRS UMR 6613 LAUM Lab Le Mans France

ISBN: (纸本)9781467319553;9781467319546

In this paper, we propose a novel acoustic localization system based on fingerprinting technique. It deploys the Time Of Arrival of CDMA signals emitted by speakers to locate a microphone. The system is inspired from our earlier proposed scheme [1] which deploys the lateration method. Here, we adopt the fingerprinting technique since it is more applicable to indoor environments. The position estimation is accomplished through nonparametric kernel regression. Performance are evaluated by experiments, performed in a hall of interns in National School of Engineers of Le Mans. Results have shown that our proposed scheme provides an accuracy of 8.5 cm within 80% precision.

关键词： Acoustic localization fingerprinting technique nonparametric kernel regression time of arrival CDMA signals

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New evidence on the role of cognitive skill in economic development

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ECONOMICS LETTERS 2012年第1期117卷 123-126页

作者： Laurini, Marcio Poletti Andrade, Eduardo de Carvalho Av Presidente Wilson 118 BR-20030020 Rio De Janeiro Brazil

This paper presents new evidence on the role of cognitive skills in promoting economic growth rate, using a within country dataset (Brazilian municipalities' dataset) and nonparametric kernel regression estimation... 详细信息

关键词： Cognitive skill Human capital nonparametric kernel regression

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Indoor Localization with Channel Impulse Response Based Fingerprint and nonparametric regression

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2010年第3期9卷 1120-1127页

作者： Jin, Yunye Soh, Wee-Seng Wong, Wai-Choong Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore

In this paper, we propose a fingerprint-based localization scheme that exploits the location dependency of the channel impulse response (CIR). We approximate the CIR by applying Inverse Fourier Transform to the receiver's channel estimation. The amplitudes of the approximated CIR (ACIR) vector are further transformed into the logarithmic scale to ensure that elements in the ACIR vector contribute fairly to the location estimation, which is accomplished through nonparametric kernel regression. As shown in our simulations, when both the number of access points and density of training locations are the same, our proposed scheme displays significant advantages in localization accuracy, compared to other fingerprint-based methods found in the literature. Moreover, absolute localization accuracy of the proposed scheme is shown to be resilient to the real time environmental changes caused by human bodies with random positions and orientations.

关键词： Indoor localization fingerprinting channel impulse response nonparametric kernel regression

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Aid and investment in LDCs: A robust approach

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JOURNAL OF INTERNATIONAL TRADE & ECONOMIC DEVELOPMENT 2010年第2期19卷 319-349页

作者： Gyimah-Brempong, Kwabena Racine, Jeffrey S. Univ S Florida Dept Econ Tampa FL 33620 USA McMaster Univ Dept Econ Hamilton ON L8S 4M4 Canada

This paper uses panel data and the Local Linear kernel Estimator (LLKE), to investigate the effects of aid on physical capital investment in developing countries. Specifically, we investigate the robustness of the relationship between aid and physical capital investment in Less Developed countries (LDCs) using two different measures of aid and five measures of the policy environment. We find that external aid has a positive and significant impact on physical capital investment given the support of the sample data we use. This effect is robust to the measurement of aid as well as the policy environment. However, the character of the positive relationship between aid and investment varies with the combination of the aid measure and the policy environment. We find that conditional on inflows, the better the policy environment, the higher the investment rate, all things being equal. The results have implications for aid research and aid policy.

关键词： aid investment developing countries nonparametric kernel regression policy impacts

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