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Digital Audio Watermarking by Quantization Embedding System 15th

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Digital Audio Watermarking by Quantization Embedding System

15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019

作者： Chen, Ching-Ju Zhao, Ming Chen, Shuo-Tsung Lin, Meng-Ju College of Future Bachelor Program in Interdisciplinary Studies National Yunlin University of Science and Technology DouliuYunlin Taiwan64002 Taiwan School of Computer Science Yangtze University Jingzhou CityHubei Province China Department of Applied Mathematics Tunghai University No. 1727 Sec. 4 Taiwan Boulevard TaichungTaiwan40704 Taiwan National Yunlin University of Science and Technology DouliuYunlin Taiwan64002 Taiwan

ISBN: (纸本)9789811397134

This paper designs a digital audio watermarking scheme using a quantization embedding system in the wavelet domain. In this paper, the relationship between the watermarked coefficients and the original coefficients is first presented and then the quantization-based embedding rules are combined into a quantization-based embedding system that incorporates a system state. For the embedding in this system, the information includes synchronization codes and the watermarks are embedded into the DWT lowest frequency sub-band coefficients, which can be described by a matrix form. Moreover, the SNR is rewritten as a performance index which is a quadratic form. Then, a nonlinear function connecting the performance index and the embedding system is derived. Accordingly, an optimization-based embedding system is proposed. Finally, the Lagrange Principle is used to obtain the optimal solution which is an important formula for watermarking. In addition, the hidden information can be extracted without knowledge of the original audio. In the experiments, the performance of the proposed system is tested and the results verify the high SNR and strong robustness against multimedia signal processing or malicious attacks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Audio watermarking

The barron space and the flow-induced function spaces for neural network models

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arXiv 2019年

作者： Weinan, E. Ma, Chao Wu, Lei Department of Mathematics Princeton University United States Program in Applied and Computational Mathematics Princeton University United States Beijing Institute of Big Data Research China

One of the key issues in the analysis of machine learning models is to identify the appropriate function space and norm for the model. This is the set of functions endowed with a quantity which can control the approximation and estimation errors by a particular machine learning model. In this paper, we address this issue for two representative neural network models: The two-layer networks and the residual neural networks. We define the Barron space and show that it is the right space for two-layer neural network models in the sense that optimal direct and inverse approximation theorems hold for functions in the Barron space. For residual neural network models, we construct the so-called flow-induced function space, and prove direct and inverse approximation theorems for this space. In addition, we show that the Rademacher complexity for bounded sets under these norms has the optimal upper bounds. Copyright © 2019, The Authors. All rights reserved.

关键词： Functional analysis

Multigoal-oriented error estimation and mesh adaptivity for fluid-structure interaction

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arXiv 2021年

作者： Ahuja, K. Endtmayer, B. Steinbach, M.C. Wick, Thomas Discipline of Computer Science and Engineering Indian Institute of Technology Indore India Leibniz Universität Hannover Institut für Angewandte Mathematik Welfengarten 1 Hannover30167 Germany Leibniz Universität Hannover Germany Université Paris-Saclay ENS Paris-Saclay LMT - Laboratoire de Mécanique et Technologie Gif-sur-Yvette91190 France Johannes Kepler University Linz Doctoral Program on Computational Mathematics Altenbergerstr. 69 LinzA-4040 Austria Austrian Academy of Sciences Johann Radon Institute for Computational and Applied Mathematics Altenbergerstr. 69 LinzA-4040 Austria

In this work, we consider multigoal-oriented error estimation for stationary fluid-structure interaction. The problem is formulated within a variational-monolithic setting using arbitrary Lagrangian-Eulerian coordinates. Employing the dual-weighted residual method for goal-oriented a posteriori error estimation, adjoint sensitivities are required. For multigoal-oriented error estimation, a combined functional is formulated such that several quantities of interest are controlled simultaneously. As localization technique for mesh refinement we employ a partition-of-unity. Our algorithmic developments are substantiated with several numerical tests such as an elastic lid-driven cavity with two goal functionals, an elastic bar in a chamber with two goal functionals, and the FSI-1 benchmark with three goal *** Codes 65N30, 65N50, 74F10 © 2021, CC BY-NC-ND.

关键词： Fluid structure interaction

Multi-target detection with application to cryo-electron microscopy

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arXiv 2019年

作者： Bendory, Tamir Boumal, Nicolas Leeb, William Levin, Eitan Singer, Amit Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Department of Mathematics Princeton University PrincetonNJ United States School of Mathematics University of Minnesota MinneapolisMN United States

We consider the multi-target detection problem of recovering a set of signals that appear multiple times at unknown locations in a noisy measurement. In the low noise regime, one can estimate the signals by first detecting occurrences, then clustering and averaging them. In the high noise regime, however, neither detection nor clustering can be performed reliably, so that strategies along these lines are destined to fail. Notwithstanding, using autocorrelation analysis, we show that the impossibility to detect and cluster signal occurrences in the presence of high noise does not necessarily preclude signal estimation. Specifically, to estimate the signals, we derive simple relations between the autocorrelations of the observation and those of the signals. These autocorrelations can be estimated accurately at any noise level given a sufficiently long measurement. To recover the signals from the observed autocorrelations, we solve a set of polynomial equations through nonlinear least-squares. We provide analysis regarding well-posedness of the task, and demonstrate numerically the effectiveness of the method in a variety of settings. The main goal of this work is to provide theoretical and numerical support for a recently proposed framework to image 3-D structures of biological macromolecules using cryo-electron microscopy in extreme noise levels. Copyright © 2019, The Authors. All rights reserved.

关键词： Autocorrelation

Assessing the reliability of wind power operations under a changing climate with a non-Gaussian bias correction

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arXiv 2020年

作者： Zhang, Jiachen Crippa, Paola Genton, Marc G. Castruccio, Stefano Department of Applied and Computational Mathematics and Statistics University of Notre Dame United States Department of Civid and Environmental Engineering and Geoscience University of Notre Dame United States Statistics Program King Abdullah University of Science and Technology Saudi Arabia

Facing increasing societal and economic pressure, many countries have established strategies to develop renewable energy portfolios, whose penetration in the market can alleviate the dependence on fossil fuels. In the case of wind, there is a fundamental question related to the resilience, and hence profitability, of future wind farms to a changing climate, given that current wind turbines have lifespans of up to thirty years. In this work we develop a new non-Gaussian method to adjust assimilated observational data to simulations and to estimate future wind, predicated on a trans-Gaussian transformation and a cluster-wise minimization of the Kullback-Leibler divergence. Future winds abundance will be determined for Saudi Arabia, a country with a recently established plan to develop a portfolio of up to 16 GW of wind energy. Further, we estimate the change in profits over future decades using additional high-resolution simulations, an improved method for vertical wind extrapolation and power curves from a collection of popular wind turbines. We find an overall increase in daily profit of $272,000 for the wind energy market for the optimal locations for wind farming in the country. Copyright © 2020, The Authors. All rights reserved.

关键词： Wind power

Meta-learning initializations for image segmentation

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arXiv 2019年

作者： Hendryx, Sean M. Leach, Andrew B. Hein, Paul D. Morrison, Clayton T. School of Information University of Arizona United States Program in Applied Mathematics University of Arizona United States

While meta-learning approaches that utilize neural network representations have made progress in few-shot image classification, reinforcement learning, and, more recently, image semantic segmentation, the training algorithms and model architectures have become increasingly specialized to the few-shot domain. A natural question that arises is how to develop learning systems that scale from few-shot to many-shot settings while yielding competitive performance in both. One scalable potential approach that does not require ensembling many models nor the computational costs of relation networks, is to meta-learn an initialization. In this work, we study first-order meta-learning of initializations for deep neural networks that must produce dense, structured predictions given an arbitrary amount of training data for a new task. Our primary contributions include (1), an extension and experimental analysis of first-order model agnostic meta-learning algorithms (including FOMAML and Reptile) to image segmentation, (2) a novel neural network architecture built for parameter efficiency and fast learning which we call EfficientLab, (3) a formalization of the generalization error of meta-learning algorithms, which we leverage to decrease error on unseen tasks, and (4) a small benchmark dataset, FP-k, for the empirical study of how meta-learning systems perform in both few- and many-shot settings. We show that meta-learned initializations for image segmentation provide value for both canonical few-shot learning problems and larger datasets, outperforming ImageNet-trained initializations for up to 400 densely labeled examples. We find that our network, with an empirically estimated optimal update procedure, yields state of the art results on the FSS-1000 dataset while only requiring one forward pass through a single model at evaluation time. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantic Segmentation

Design and Numerical Analysis of an Open-Circuit Low-Speed Wind Tunnel

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Design and Numerical Analysis of an Open-Circuit Low-Speed W...

2019 Novel Intelligent and Leading Emerging Sciences Conference, NILES 2019

作者： Seddik, Ahmed Ahmed, Marawan Ayman, Mohamed Guaily, Amr School of Engineering and Applied Sciences Nile University Mechanical Engineering Program Shaikh Zayed City12588 Egypt School of Engineering and Applied Sciences Nile University Shaikh Zayed City12588 Egypt Engineering Mathematics and Physics Department Faculty of Engineering Cairo University Giza12613 Egypt

ISBN: (纸本)9781728131733

The design and numerical analysis of a 3D open-circuit, low-speed, with a maximum velocity of 20 m/s in the test section, wind tunnel is presented. Two diffusers' angels are numerically tested in order to reduce the pumping power of the fan. The proposed design is analyzed using three-dimensional, turbulent, incompressible flow using ANSYS FLUENT. Numerical results show that the velocity as well as the pressure are uniform in the test section and no vortices are formed in the diffuser section which affirms the success of the proposed design. © 2019 IEEE.

关键词： Numerical analysis

Generation and structural characterization of Debye random media

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arXiv 2020年

作者： Ma, Zheng Torquato, Salvatore Department of Physics Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

In their seminal paper on scattering by an inhomogeneous solid, Debye and coworkers proposed a simple exponentially decaying function for the two-point correlation function of an idealized class of two-phase random media. Such Debye random media, which have been shown to be realizable, are singularly distinct from all other models of two-phase media in that they are entirely defined by their one- and two-point correlation functions. To our knowledge, there has been no determination of other microstructural descriptors of Debye random media. In this paper, we generate Debye random media in two dimensions using an accelerated Yeong-Torquato construction algorithm. We then ascertain microstructural descriptors of the constructed media, including their surface correlation functions, pore-size distributions, lineal-path function, and chord-length probability density function. Accurate semi-analytic and empirical formulas for these descriptors are devised. We compare our results for Debye random media to those of other popular models (overlapping disks and equilibrium hard disks), and find that the former model possesses a wider spectrum of hole sizes, including a substantial fraction of large holes. Our algorithm can be applied to generate other models defined by their two-point correlation functions, and their other microstructural descriptors can be determined and analyzed by the procedures laid out here. Copyright © 2020, The Authors. All rights reserved.

关键词： Random processes

Sensitivity kernels for inferring Lorentz stresses from normal-mode frequency splittings in the Sun

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arXiv 2020年

作者： Das, Srijan Bharati Chakraborty, Tuneer Hanasoge, Shravan M. Tromp, Jeroen Department of Geosciences Princeton University PrincetonNJ United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Mumbai India Department of Geosciences and Program in Applied & Computational Mathematics Princeton University Princeton NJ United States

Departures from standard spherically symmetric solar models, in the form of perturbations such as global and local-scale flows and structural asphericities, result in the splitting of eigenfrequencies in the observed spectrum of solar oscillations. Drawing from prevalent ideas in normal-mode coupling theory in geophysical literature, we devise a procedure that enables the computation of sensitivity kernels for general Lorentz stress fields in the Sun. Mode coupling due to any perturbation requires careful consideration of self- and cross-coupling of multiplets. Invoking the isolated-multiplet approximation allows for limiting the treatment to purely self-coupling, requiring significantly less computational resources. We identify the presence of such isolated multiplets under the effect of Lorentz stresses in the Sun. Currently, solar missions allow precise measurements of self-coupling of multiplets via "a-coefficients" and the cross-spectral correlation signal which enables the estimation of the "structure coefficients". We demonstrate the forward problem for both self-coupling (a-coefficients) and cross-coupling (structure coefficients). In doing so, we plot the self-coupling kernels and estimate a-coefficients arising from a combination of deep-toroidal and surface-dipolar axisymmetric fields. We also compute the structure coefficients for an arbitrary general magnetic field (real and solenoidal) and plot the corresponding "splitting function", a convenient way to visualize the splitting of multiplets under 3D internal perturbations. The results discussed in this paper pave the way to formally pose an inverse problem, and infer solar internal magnetic fields. Copyright © 2020, The Authors. All rights reserved.

关键词： Chemical reactions