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Journal articles on the topic 'Exact recovery'

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Published: 9 November 2024

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1

Andrecut, M. "Exact Fourier spectrum recovery." Physics Letters A 377, no. 1-2 (December 2012): 1–6. http://dx.doi.org/10.1016/j.physleta.2012.10.018.

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2

Tsuda, Seiya, Yuji Iwahori, M. K. Bhuyan, Robert J. Woodham, and Kunio Kasugai. "Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network." International Journal of Biomedical Imaging 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/109804.

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Medical diagnosis judges the status of polyp from the size and the 3D shape of the polyp from its medical endoscope image. However the medical doctor judges the status empirically from the endoscope image and more accurate 3D shape recovery from its 2D image has been demanded to support this judgment. As a method to recover 3D shape with high speed, VBW (Vogel-Breuß-Weickert) model is proposed to recover 3D shape under the condition of point light source illumination and perspective projection. However, VBW model recovers the relative shape but there is a problem that the shape cannot be recovered with the exact size. Here, shape modification is introduced to recover the exact shape with modification from that with VBW model. RBF-NN is introduced for the mapping between input and output. Input is given as the output of gradient parameters of VBW model for the generated sphere. Output is given as the true gradient parameters of true values of the generated sphere. Learning mapping with NN can modify the gradient and the depth can be recovered according to the modified gradient parameters. Performance of the proposed approach is confirmed via computer simulation and real experiment.
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3

Cheded, L. "Exact recovery of higher order moments." IEEE Transactions on Information Theory 44, no. 2 (March 1998): 851–58. http://dx.doi.org/10.1109/18.661534.

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4

Berthet, Quentin, Philippe Rigollet, and Piyush Srivastava. "Exact recovery in the Ising blockmodel." Annals of Statistics 47, no. 4 (August 2019): 1805–34. http://dx.doi.org/10.1214/17-aos1620.

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5

Dym, Nadav, and Yaron Lipman. "Exact Recovery with Symmetries for Procrustes Matching." SIAM Journal on Optimization 27, no. 3 (January 2017): 1513–30. http://dx.doi.org/10.1137/16m1078628.

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6

Abbe, Emmanuel, Afonso S. Bandeira, and Georgina Hall. "Exact Recovery in the Stochastic Block Model." IEEE Transactions on Information Theory 62, no. 1 (January 2016): 471–87. http://dx.doi.org/10.1109/tit.2015.2490670.

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7

Duval, Vincent, and Gabriel Peyré. "Exact Support Recovery for Sparse Spikes Deconvolution." Foundations of Computational Mathematics 15, no. 5 (October 9, 2014): 1315–55. http://dx.doi.org/10.1007/s10208-014-9228-6.

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8

You, Qing Shan, and Qun Wan. "Principal Component Pursuit with Weighted Nuclear Norm." Applied Mechanics and Materials 513-517 (February 2014): 1722–26. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1722.

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Principal Component Pursuit (PCP) recovers low-dimensional structures from a small set of linear measurements, such as low rank matrix and sparse matrix. Pervious works mainly focus on exact recovery without additional noise. However, in many applications the observed measurements are corrupted by an additional white Gaussian noise (AWGN). In this paper, we model the recovered matrix the sum a low-rank matrix, a sparse matrix and an AWGN. We propose a weighted PCP for the recovery matrix, which is solved by alternating direction method. Numerical results show that the reconstructions performance of weighted PCP outperforms the classical PCP in term of accuracy.
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9

Chen, Xiaohui, and Yun Yang. "Cutoff for Exact Recovery of Gaussian Mixture Models." IEEE Transactions on Information Theory 67, no. 6 (June 2021): 4223–38. http://dx.doi.org/10.1109/tit.2021.3063155.

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10

Hajek, Bruce, Yihong Wu, and Jiaming Xu. "Achieving Exact Cluster Recovery Threshold via Semidefinite Programming." IEEE Transactions on Information Theory 62, no. 5 (May 2016): 2788–97. http://dx.doi.org/10.1109/tit.2016.2546280.

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11

Yang, Zai, and Lihua Xie. "Exact Joint Sparse Frequency Recovery via Optimization Methods." IEEE Transactions on Signal Processing 64, no. 19 (October 1, 2016): 5145–57. http://dx.doi.org/10.1109/tsp.2016.2576422.

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12

Lerman, Gilad, Yunpeng Shi, and Teng Zhang. "Exact Camera Location Recovery by Least Unsquared Deviations." SIAM Journal on Imaging Sciences 11, no. 4 (January 2018): 2692–721. http://dx.doi.org/10.1137/17m115061x.

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13

Hand, Paul, Choongbum Lee, and Vladislav Voroninski. "ShapeFit: Exact Location Recovery from Corrupted Pairwise Directions." Communications on Pure and Applied Mathematics 71, no. 1 (November 16, 2017): 3–50. http://dx.doi.org/10.1002/cpa.21727.

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14

KONG, LINGCHEN, and NAIHUA XIU. "EXACT LOW-RANK MATRIX RECOVERY VIA NONCONVEX SCHATTEN p-MINIMIZATION." Asia-Pacific Journal of Operational Research 30, no. 03 (June 2013): 1340010. http://dx.doi.org/10.1142/s0217595913400101.

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The low-rank matrix recovery (LMR) arises in many fields such as signal and image processing, quantum state tomography, magnetic resonance imaging, system identification and control, and it is generally NP-hard. Recently, Majumdar and Ward [Majumdar, A and RK Ward (2011). An algorithm for sparse MRI reconstruction by Schatten p-norm minimization. Magnetic Resonance Imaging, 29, 408–417]. had successfully applied nonconvex Schatten p-minimization relaxation of LMR in magnetic resonance imaging. In this paper, our main aim is to establish RIP theoretical result for exact LMR via nonconvex Schatten p-minimization. Carefully speaking, letting [Formula: see text] be a linear transformation from ℝm×n into ℝs and r be the rank of recovered matrix X ∈ ℝm×n, and if [Formula: see text] satisfies the RIP condition [Formula: see text] for a given positive integer k ∈ {1, 2, …, m – r}, then r-rank matrix can be exactly recovered. In particular, we obtain a uniform bound on restricted isometry constant [Formula: see text] for any p ∈ (0, 1] for LMR via Schatten p-minimization.
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15

Eswaraiah, R., and E. Sreenivasa Reddy. "Medical Image Watermarking Technique for Accurate Tamper Detection in ROI and Exact Recovery of ROI." International Journal of Telemedicine and Applications 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/984646.

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In telemedicine while transferring medical images tampers may be introduced. Before making any diagnostic decisions, the integrity of region of interest (ROI) of the received medical image must be verified to avoid misdiagnosis. In this paper, we propose a novel fragile block based medical image watermarking technique to avoid embedding distortion inside ROI, verify integrity of ROI, detect accurately the tampered blocks inside ROI, and recover the original ROI with zero loss. In this proposed method, the medical image is segmented into three sets of pixels: ROI pixels, region of noninterest (RONI) pixels, and border pixels. Then, authentication data and information of ROI are embedded in border pixels. Recovery data of ROI is embedded into RONI. Results of experiments conducted on a number of medical images reveal that the proposed method produces high quality watermarked medical images, identifies the presence of tampers inside ROI with 100% accuracy, and recovers the original ROI without any loss.
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16

Zhao, Feng, Min Ye, and Shao-Lun Huang. "Exact Recovery of Stochastic Block Model by Ising Model." Entropy 23, no. 1 (January 2, 2021): 65. http://dx.doi.org/10.3390/e23010065.

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In this paper, we study the phase transition property of an Ising model defined on a special random graph—the stochastic block model (SBM). Based on the Ising model, we propose a stochastic estimator to achieve the exact recovery for the SBM. The stochastic algorithm can be transformed into an optimization problem, which includes the special case of maximum likelihood and maximum modularity. Additionally, we give an unbiased convergent estimator for the model parameters of the SBM, which can be computed in constant time. Finally, we use metropolis sampling to realize the stochastic estimator and verify the phase transition phenomenon thfough experiments.
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17

Gao, Zheng, and Stilian Stoev. "Fundamental limits of exact support recovery in high dimensions." Bernoulli 26, no. 4 (November 2020): 2605–38. http://dx.doi.org/10.3150/20-bej1197.

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18

Tran, Giang, and Rachel Ward. "Exact Recovery of Chaotic Systems from Highly Corrupted Data." Multiscale Modeling & Simulation 15, no. 3 (January 2017): 1108–29. http://dx.doi.org/10.1137/16m1086637.

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19

Hajek, Bruce, Yihong Wu, and Jiaming Xu. "Achieving Exact Cluster Recovery Threshold via Semidefinite Programming: Extensions." IEEE Transactions on Information Theory 62, no. 10 (October 2016): 5918–37. http://dx.doi.org/10.1109/tit.2016.2594812.

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20

Yin, Zi-qiang. "Exact wavefront recovery with tilt from lateral shear interferograms." Applied Optics 48, no. 14 (May 7, 2009): 2760. http://dx.doi.org/10.1364/ao.48.002760.

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21

Rahnama Rad, Kamiar. "Nearly Sharp Sufficient Conditions on Exact Sparsity Pattern Recovery." IEEE Transactions on Information Theory 57, no. 7 (July 2011): 4672–79. http://dx.doi.org/10.1109/tit.2011.2145670.

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22

Wang, L., and A. Singer. "Exact and stable recovery of rotations for robust synchronization." Information and Inference 2, no. 2 (September 27, 2013): 145–93. http://dx.doi.org/10.1093/imaiai/iat005.

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23

Samani, Nozar, and M. Pasandi. "Retracted:A Single Recovery Type Curve from Theis' Exact Solution." Ground Water 41, no. 5 (September 2003): 602–7. http://dx.doi.org/10.1111/j.1745-6584.2003.tb02398.x.

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24

Dym, Nadav. "Exact Recovery with Symmetries for the Doubly Stochastic Relaxation." SIAM Journal on Applied Algebra and Geometry 2, no. 3 (January 2018): 462–88. http://dx.doi.org/10.1137/17m1132264.

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25

Konyagin, S. V., Yu V. Malykhin, and K. S. Ryutin. "On exact recovery of sparse vectors from linear measurements." Mathematical Notes 94, no. 1-2 (July 2013): 107–14. http://dx.doi.org/10.1134/s0001434613070109.

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26

Li, Chao, Mohammad Emtiyaz Khan, Zhun Sun, Gang Niu, Bo Han, Shengli Xie, and Qibin Zhao. "Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition Under Reshuffling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4602–9. http://dx.doi.org/10.1609/aaai.v34i04.5890.

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Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis. The numerical defects of TD methods, however, limit their practical applications on real-world data. As an alternative, convex tensor decomposition (CTD) was proposed to alleviate these problems, but its exact-recovery property is not properly addressed so far. To this end, we focus on latent convex tensor decomposition (LCTD), a practically widely-used CTD model, and rigorously prove a sufficient condition for its exact-recovery property. Furthermore, we show that such property can be also achieved by a more general model than LCTD. In the new model, we generalize the classic tensor (un-)folding into reshuffling operation, a more flexible mapping to relocate the entries of the matrix into a tensor. Armed with the reshuffling operations and exact-recovery property, we explore a totally novel application for (generalized) LCTD, i.e., image steganography. Experimental results on synthetic data validate our theory, and results on image steganography show that our method outperforms the state-of-the-art methods.
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27

Boikov, Ilia V., and Nikolay P. Krivulin. "Non-stationary dynamic system characteristics recovery from three test signals." Izmeritel`naya Tekhnika, no. 3 (2020): 9–15. http://dx.doi.org/10.32446/0368-1025it.2020-3-9-15.

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Algorithms of exact restoration in an analytical form of dynamic characteristics of non-stationary dynamic systems are constructed. Non-stationary continuous dynamical systems modeled by Volterra integral equations of the first kind and non-stationary discrete dynamical systems modeled by discrete analogues of Volterra integral equations of the first kind are considered.The article consists of an introduction and three sections: 1) The exact restoration of the dynamic characteristics of continuous systems, 2) The restoration of the transition characteristics of discrete systems, 3) Conclusions. The introduction provides a statement of the problem and provides an overview of dynamical systems for which algorithms for exact reconstruction in ananalytical form of the impulse response (in the case of continuous systems) and the transition characteristic (in the case of discrete systems) are constructed. In the first section, the algorithm is constructed for the exact reconstruction of the impulse response of an non-stationary continuous dynamic system from three interconnected input signals. The first signal may be arbitrary, the second and third signals are associated with the first signal by integral operator. The exact formula for the Laplace transform of the impulse response, represented by an algebraic expression from the Laplace transform of the system output signals, is given. A model example illustrating the effectiveness of the algorithm is given. The practical application of the presented algorithm isdiscussed. In the second section, an algorithm is constructed for the exact reconstruction of the transition response of a non-stationary discrete dynamical system from three input signals that are interconnected. The first signal may be arbitrary, the second and third signals are associated with the first summing operator. The exact formula of the Z-transform of the transition characteristic is presented, which is represented by an algebraic expression from the Z-transform of the system output signals. A model example is given. The “Conclusions” section provides a summary of the results presented in the article and describes the dynamic systems to which the proposed algorithms can be extended.
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28

Han, Zhi, Jianjun Wang, Jia Jing, and Hai Zhang. "A Simple Gaussian Measurement Bound for Exact Recovery of Block-Sparse Signals." Discrete Dynamics in Nature and Society 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/104709.

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We present a probabilistic analysis on conditions of the exact recovery of block-sparse signals whose nonzero elements appear in fixed blocks. We mainly derive a simple lower bound on the necessary number of Gaussian measurements for exact recovery of such block-sparse signals via the mixedl2/lq (0<q≤1)norm minimization method. In addition, we present numerical examples to partially support the correctness of the theoretical results. The obtained results extend those known for the standardlqminimization and the mixedl2/l1minimization methods to the mixedl2/lq (0<q≤1)minimization method in the context of block-sparse signal recovery.
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29

Nishimura, Koichi, Saya Nakamura, Masaaki Kusunose, Kazuhito Nakayasu, Ryo Sanda, Yoshinori Hasegawa, and Toru Oga. "Comparison of patient-reported outcomes during acute exacerbations of chronic obstructive pulmonary disease." BMJ Open Respiratory Research 5, no. 1 (October 2018): e000305. http://dx.doi.org/10.1136/bmjresp-2018-000305.

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IntroductionThe aim of this study was to investigate which patient-reported outcome measure was the best during the recovery phase from severe exacerbation of chronic obstructive pulmonary disease (COPD).MethodsThe Exacerbations of Chronic Pulmonary Disease Tool (EXACT), the COPD Assessment Test (CAT), the St George’s Respiratory Questionnaire (SGRQ), the Dyspnoea-12 (D-12) and the Hyland Scale (global scale) were recorded every week for the first month and at 2 and 3 months in 33 hospitalised subjects with acute exacerbation of COPD (AECOPD).ResultsOn the day of admission (day 1), the internal consistency of the EXACT total score was high (Cronbach’s alpha coefficient=0.89). The EXACT total, CAT, SGRQ total and Hyland Scale scores obtained on day 1 appeared to be normally distributed. Neither floor nor ceiling effects were observed for the EXACT total and SGRQ total scores. The EXACT total score improved from 50.5±12.4 to 32.5±14.3, and the CAT score also improved from 24.4±8.5 to 13.5±8.4 during the first 2 weeks, and the effect sizes (ES) of the EXACT total and CAT score were −1.40 and −1.36, respectively. The SGRQ, Hyland Scale and D-12 were less responsive, with ES of −0.59, 0.96 and −0.90, respectively.DiscussionThe EXACT total and CAT scores are shown to be more responsive measures during the recovery phase from severe exacerbation. Considering the conceptual framework, it is recommended that the EXACT total score may be the best measure during the recovery phase from AECOPD. The reasons for the outstanding responsiveness of the CAT are still unknown.
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30

sci, Anping Liao. "The Exact Recovery of Sparse Signals Via Orthogonal Matching Pursuit." Journal of Computational Mathematics 34, no. 1 (June 2016): 70–86. http://dx.doi.org/10.4208/jcm.1510-m2015-0284.

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31

Ye, Min. "Exact Recovery and Sharp Thresholds of Stochastic Ising Block Model." IEEE Transactions on Information Theory 67, no. 12 (December 2021): 8207–35. http://dx.doi.org/10.1109/tit.2021.3117264.

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32

Ye, Min. "Exact Recovery and Sharp Thresholds of Stochastic Ising Block Model." IEEE Transactions on Information Theory 67, no. 12 (December 2021): 8207–35. http://dx.doi.org/10.1109/tit.2021.3117264.

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33

Herzet, Cedric, Charles Soussen, Jerome Idier, and Remi Gribonval. "Exact Recovery Conditions for Sparse Representations With Partial Support Information." IEEE Transactions on Information Theory 59, no. 11 (November 2013): 7509–24. http://dx.doi.org/10.1109/tit.2013.2278179.

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34

Dan, Wei, and Yu Fu. "Exact support recovery via orthogonal matching pursuit from noisy measurements." Electronics Letters 52, no. 17 (August 2016): 1497–99. http://dx.doi.org/10.1049/el.2016.1893.

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35

Determe, Jean-Francois, Jerome Louveaux, Laurent Jacques, and Francois Horlin. "On The Exact Recovery Condition of Simultaneous Orthogonal Matching Pursuit." IEEE Signal Processing Letters 23, no. 1 (January 2016): 164–68. http://dx.doi.org/10.1109/lsp.2015.2506989.

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36

Zhai, Dede, Shanyong Chen, Shuai Xue, and Ziqiang Yin. "Exact recovery of wavefront from multishearing interferograms in spatial domain." Applied Optics 55, no. 28 (September 29, 2016): 8063. http://dx.doi.org/10.1364/ao.55.008063.

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37

Yang, Jung-Min. "Exact fault recovery for asynchronous sequential machines with output bursts." Automatica 97 (November 2018): 115–20. http://dx.doi.org/10.1016/j.automatica.2018.08.001.

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38

Qu, Qing, Xiao Li, and Zhihui Zhu. "Exact Recovery of Multichannel Sparse Blind Deconvolution via Gradient Descent." SIAM Journal on Imaging Sciences 13, no. 3 (January 2020): 1630–52. http://dx.doi.org/10.1137/19m1291327.

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39

Saad, Hussein, and Aria Nosratinia. "Exact Recovery in Community Detection With Continuous-Valued Side Information." IEEE Signal Processing Letters 26, no. 2 (February 2019): 332–36. http://dx.doi.org/10.1109/lsp.2018.2889920.

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40

Trede, Dennis. "Exact support recovery for linear inverse problems with sparsity constraints." Methods and Applications of Analysis 18, no. 1 (2011): 105–10. http://dx.doi.org/10.4310/maa.2011.v18.n1.a7.

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41

Ovall, Jeffrey S. "Asymptotically exact functional error estimators based on superconvergent gradient recovery." Numerische Mathematik 102, no. 3 (November 15, 2005): 543–58. http://dx.doi.org/10.1007/s00211-005-0655-9.

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42

Guo, Ping, Changhua Wei, Wenjun Xiong, and Chunlan Zhao. "Exact Boundary Controller Design for a Kind of Enhanced Oil Recovery Models." Abstract and Applied Analysis 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/747092.

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The exact boundary controllability of a class of enhanced oil recovery systems is discussed in this paper. With a simple transformation, the enhanced oil recovery model is first affirmed to be neither genuinely nonlinear nor linearly degenerate. It is then shown that the enhanced oil recovery system with nonlinear boundary conditions is exactly boundary controllable by applying a constructed method. Moreover, an interval of the control time is presented to not only give the optimal control time but also show the time for avoiding the blowup of the controllable solution. Finally, an example is given to illustrate the effectiveness of the proposed criterion.
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43

Osipenko, Konstantin Yur'evich. "Recovery of analytic functions that is exact on subspaces of entire functions." Sbornik: Mathematics 215, no. 3 (2024): 383–400. http://dx.doi.org/10.4213/sm9976e.

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A family of optimal recovery methods is developed for the recovery of analytic functions in a strip and their derivatives from inaccurately specified trace of the Fourier transforms of these functions on the real axis. In addition, the methods must be exact on some subspaces of entire functions. Bibliography: 12 titles.
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44

Wang, Runsong, Xuelian Li, Juntao Gao, Hui Li, and Baocang Wang. "Quantum rotational cryptanalysis for preimage recovery of round-reduced Keccak." Quantum Information & Computation 23, no. 3&4 (February 2023): 223–34. http://dx.doi.org/10.26421/qic23.3-4-3.

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The Exclusive-OR Sum-of-Product (ESOP) minimization problem has long been of interest to the research community because of its importance in classical logic design (including low-power design and design for test), reversible logic synthesis, and knowledge discovery, among other applications. However, no exact minimal minimization method has been presented for more than seven variables on arbitrary functions. This paper presents a novel quantum-classical hybrid algorithm for the exact minimal ESOP minimization of incompletely specified Boolean functions. This algorithm constructs oracles from sets of constraints and leverages the quantum speedup offered by Grover's algorithm to find solutions to these oracles, thereby improving over classical algorithms. Improved encoding of ESOP expressions results in substantially fewer decision variables compared to many existing algorithms for many classes of Boolean functions. This paper also extends the idea of exact minimal ESOP minimization to additionally minimize the cost of realizing an ESOP expression as a quantum circuit. To the extent of the authors' knowledge, such a method has never been published. This algorithm was tested on completely and incompletely specified Boolean functions via quantum simulation.
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45

Fuchs, J. J. "Recovery of Exact Sparse Representations in the Presence of Bounded Noise." IEEE Transactions on Information Theory 51, no. 10 (October 2005): 3601–8. http://dx.doi.org/10.1109/tit.2005.855614.

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46

Cole, Sam, and Yizhe Zhu. "Exact recovery in the hypergraph stochastic block model: A spectral algorithm." Linear Algebra and its Applications 593 (May 2020): 45–73. http://dx.doi.org/10.1016/j.laa.2020.01.039.

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47

Jaulmes, Luc, Miquel Moreto, Eduard Ayguade, Jesus Labarta, Mateo Valero, and Marc Casas. "Asynchronous and Exact Forward Recovery for Detected Errors in Iterative Solvers." IEEE Transactions on Parallel and Distributed Systems 29, no. 9 (September 1, 2018): 1961–74. http://dx.doi.org/10.1109/tpds.2018.2817524.

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48

Wen, Jinming, Zhengchun Zhou, Jian Wang, Xiaohu Tang, and Qun Mo. "A Sharp Condition for Exact Support Recovery With Orthogonal Matching Pursuit." IEEE Transactions on Signal Processing 65, no. 6 (March 15, 2017): 1370–82. http://dx.doi.org/10.1109/tsp.2016.2634550.

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49

Xiao, Dafei, Qian Ye, Zhan Tong, Binbin Xiang, and Na Wang. "Exact phase recovery applying only phase modulations in an isolated region." Optics and Lasers in Engineering 134 (November 2020): 106278. http://dx.doi.org/10.1016/j.optlaseng.2020.106278.

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50

Cui, Angang, Jigen Peng, and Haiyang Li. "Exact recovery low-rank matrix via transformed affine matrix rank minimization." Neurocomputing 319 (November 2018): 1–12. http://dx.doi.org/10.1016/j.neucom.2018.05.092.

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