Measurement driven quantum evolution

We study the problem of mapping an unknown mixed quantum state onto a known pure state without the use of unitary transformations. This is achieved with the help of sequential measurements of two non-commuting observables only. We show that the overall success probability is maximized in the case of measuring two observables whose eigenstates define mutually unbiased bases. We find that for this optimal case the success probability quickly converges to unity as the number of measurement processes increases and that it is almost independent of the initial state. In particular, we show that to guarantee a success probability close to one the number of consecutive measurements must be larger than the dimension of the Hilbert space. We connect these results to quantum copying, quantum deleting and entanglement generation.Comment: 7 pages, 1 figur

Natural genetic diversity of nutritive value traits in the genus Cynodon

The Cynodon spp. collection maintained by United States Department of Agriculture National Plant Germplasm System (USDA-NPGS) has limited information on nutritive value (NV) traits. In this study, crude protein (CP), phosphorous concentration (P), in vitro digestible organic matter (IVDOM), and neutral detergent fiber (NDF) were determined to (i) estimate genetic parameters for NV, (ii) obtain genetic values for the whole population across two harvests, (iii) estimate genotype by harvest interaction (GHI) for NV traits, and (iv) select accessions exhibiting improved NV traits compared to ‘Tifton 850 . The experiment was setup as a row-column design with two replicates and augmented representation of controls: Tifton 85, ‘Jiggs’, and ‘Coastal’. The whole-population was harvested twice, and data were analyzed using linear mixed models with repeated measures. In addition, a selected population of 15 genotypes were evaluated across 11 harvests to determine the extent of GHI. Genetic parameters revealed the presence of significant genetic variability, indicating potential improvements for NV through breeding. Specifically, P and IVDOM presented large variation, while NDF had lower diversity but some accessions exhibited lower NDF than Tifton 85. Low GHI, except for IVDOM, indicated genotypic stability and potential for selecting improved accessions under fewer harvests. Breeding line 240, PI-316510, and PI-3166536 presented superior NV than Tifton 85

O mercado da tilápia - 3º trimestre de 2015.

Comportamento do varejo - mercado nacional. Evolução dos preços. no primeiro semestre de 2015. Categorias comercializadas. O setor externo. O potencial da tilápia avança sobre o mercado dos peixes marinhos. A tilápia é um peixe caro? O preço da praticidade. Peixarias x Supermercados. O setor externo.bitstream/item/131701/1/tilapia-5.pd

O mercado da tilápia - 1º trimestre de 2015.

Comportamento do varejo - mercado nacional. Categorias comercializadas. O setor externo.bitstream/item/124994/1/cnpasainf3.pd

A Novel Methodology for Power Quality Disturbances Detection and Classification in Industrial Facilities

The industrial facilities inject noise to the power line. Concerning this issue, researchers are focusing their effort on developing new techniques for analyzing the power quality of the power net. This work presents a novel methodology for power quality disturbances detection and classification based on the Harris hawks optimization algorithm and discrete wavelet transforms decomposition of the signal

A Novel Methodology for Power Quality Disturbances Detection and Classification in Industrial Facilities

819-823The industrial facilities inject noise to the power line. Concerning this issue, researchers are focusing their effort on developing new techniques for analyzing the power quality of the power net. This work presents a novel methodology for power quality disturbances detection and classification based on the Harris hawks optimization algorithm and discrete wavelet transforms decomposition of the signal

Release of a New Forage Bermudagrass Cultivar from the USDA-NPGS Cynodon Collection

Warm-season perennial grasses are the backbone of the pasture-based livestock industry in the southeastern USA. In Florida specifically, bahiagrass (Paspalum notatum Flugge) and bermudagrass (Cynodon spp.) support 1 million head of cattle and 15,000 beef cattle operations. Bermudagrass is the most widely planted forage species in the southeastern USA, planted in approximately 15 million ha and used for grazing, hay and silage. The genus Cynodon is native to southern Africa and germplasm collections have revealed a high degree of genetic variability within the genus. The United States Department of Agriculture National Plant Germplasm System (USDA-NPGS) maintains a collection of bermudagrass plant introduction (PIs) in Griffin, GA, USA and the USDA Georgia Coastal Plains Experiment Station, Tifton, GA, maintains additional forage germplasm. Multi-location trials were established in 2014 in four states (FL, GA, NC and OK) to screen the collection for herbage accumulation (HA) and nutritive value (NV). Due to the large genotype by environment interaction for HA across states, we focused on selecting accessions adapted to South Georgia and Florida. Several PIs showed improved HA and NV compared to ‘Tifton 85’ across several trials and years. Particularly, PI 316510 produced high HA in Citra, FL and Tifton, GA, had improved NV traits, and faster establishment compared to Tifton 85. We confirmed that PI 316510 is tetraploid by chromosome counts and flow cytometry. The PI 316510 has been released by the University of Florida under the name “Newell”

2011-2012 UNLV McNair Journal

Journal articles based on research conducted by undergraduate students in the McNair Scholars Program Table of Contents Biography of Dr. Ronald E. McNair Statements: Dr. Neal J. Smatresk, UNLV President Dr. Juanita P. Fain, Vice President of Student Affairs Dr. William W. Sullivan, Associate Vice President for Retention and Outreach Mr. Keith Rogers, Deputy Executive Director of the Center for Academic Enrichment and Outreach McNair Scholars Institute Staf

Standardizing Single-Frame Phase Singularity Identification Algorithms and Parameters in Phase Mapping During Human Atrial Fibrillation

[EN] Purpose Recent investigations failed to reproduce the positive rotor-guided ablation outcomes shown by initial studies for treating persistent atrial fibrillation (persAF). Phase singularity (PS) is an important feature for AF driver detection, but algorithms for automated PS identification differ. We aim to investigate the performance of four different techniques for automated PS detection. Methods 2048-channel virtual electrogram (VEGM) and electrocardiogram signals were collected for 30 s from 10 patients undergoing persAF ablation. QRST-subtraction was performed and VEGMs were processed using sinusoidal wavelet reconstruction. The phase was obtained using Hilbert transform. PSs were detected using four algorithms: (1) 2D image processing based and neighbor-indexing algorithm; (2) 3D neighbor-indexing algorithm; (3) 2D kernel convolutional algorithm estimating topological charge; (4) topological charge estimation on 3D mesh. PS annotations were compared using the structural similarity index (SSIM) and Pearson's correlation coefficient (CORR). Optimized parameters to improve detection accuracy were found for all four algorithms usingF(beta)score and 10-fold cross-validation compared with manual annotation. Local clustering with density-based spatial clustering of applications with noise (DBSCAN) was proposed to improve algorithms 3 and 4. Results The PS density maps created by each algorithm with default parameters were poorly correlated. Phase gradient threshold and search radius (or kernels) were shown to affect PS detections. The processing times for the algorithms were significantly different (p< 0.0001). TheF(beta)scores for algorithms 1, 2, 3, 3 + DBSCAN, 4 and 4 + DBSCAN were 0.547, 0.645, 0.742, 0.828, 0.656, and 0.831. Algorithm 4 + DBSCAN achieved the best classification performance with acceptable processing time (2.0 +/- 0.3 s). Conclusion AF driver identification is dependent on the PS detection algorithms and their parameters, which could explain some of the inconsistencies in rotor-guided ablation outcomes in different studies. For 3D triangulated meshes, algorithm 4 + DBSCAN with optimal parameters was the best solution for real-time, automated PS detection due to accuracy and speed. Similarly, algorithm 3 + DBSCAN with optimal parameters is preferred for uniform 2D meshes. Such algorithms - and parameters - should be preferred in future clinical studies for identifying AF drivers and minimizing methodological heterogeneities. This would facilitate comparisons in rotor-guided ablation outcomes in future works.This work was supported by the NIHR Leicester Biomedical Research Centre, UK. XL received research grants from Medical Research Council UK (MRC DPFS Ref: MR/S037306/1). TA received research grants from the British Heart Foundation (BHF Project Grant No. PG/18/33/33780), BHF Research Accelerator Award funding and Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP, Brazil, Grant No. 2017/00319-8). MG research was funded by a research grant from the Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain: PI13-00903). GN received funding from the British Heart Foundation (BHF Programme Grant, RG/17/3/32774).Li, X.; Almeida, TP.; Dastagir, N.; Guillem Sánchez, MS.; Salinet, J.; Chu, GS.; Stafford, PJ.... (2020). Standardizing Single-Frame Phase Singularity Identification Algorithms and Parameters in Phase Mapping During Human Atrial Fibrillation. Frontiers in Physiology. 11:1-16. https://doi.org/10.3389/fphys.2020.00869S11611ALHUSSEINI, M., VIDMAR, D., MECKLER, G. L., KOWALEWSKI, C. A., SHENASA, F., WANG, P. J., … RAPPEL, W.-J. (2017). Two Independent Mapping Techniques Identify Rotational Activity Patterns at Sites of Local Termination During Persistent Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 28(6), 615-622. doi:10.1111/jce.13177Allessie, M. A., de Groot, N. M. S., Houben, R. P. M., Schotten, U., Boersma, E., Smeets, J. L., & Crijns, H. J. (2010). Electropathological Substrate of Long-Standing Persistent Atrial Fibrillation in Patients With Structural Heart Disease. Circulation: Arrhythmia and Electrophysiology, 3(6), 606-615. doi:10.1161/circep.109.910125Benharash, P., Buch, E., Frank, P., Share, M., Tung, R., Shivkumar, K., & Mandapati, R. (2015). Quantitative Analysis of Localized Sources Identified by Focal Impulse and Rotor Modulation Mapping in Atrial Fibrillation. Circulation: Arrhythmia and Electrophysiology, 8(3), 554-561. doi:10.1161/circep.115.002721BRAY, M.-A., LIN, S.-F., ALIEV, R. R., ROTH, B. J., & WIKSWO, J. P. (2001). Experimental and Theoretical Analysis of Phase Singularity Dynamics in Cardiac Tissue. Journal of Cardiovascular Electrophysiology, 12(6), 716-722. doi:10.1046/j.1540-8167.2001.00716.xBray, M.-A., & Wikswo, J. P. (2002). Use of topological charge to determine filament location and dynamics in a numerical model of scroll wave activity. IEEE Transactions on Biomedical Engineering, 49(10), 1086-1093. doi:10.1109/tbme.2002.803516Buch, E., Share, M., Tung, R., Benharash, P., Sharma, P., Koneru, J., … Shivkumar, K. (2016). Long-term clinical outcomes of focal impulse and rotor modulation for treatment of atrial fibrillation: A multicenter experience. Heart Rhythm, 13(3), 636-641. doi:10.1016/j.hrthm.2015.10.031Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679-698. doi:10.1109/tpami.1986.4767851Clayton, R. H., & Nash, M. P. (2015). Analysis of Cardiac Fibrillation Using Phase Mapping. Cardiac Electrophysiology Clinics, 7(1), 49-58. doi:10.1016/j.ccep.2014.11.011Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning - ICML ’06. doi:10.1145/1143844.1143874De Groot, N. M. S., Houben, R. P. M., Smeets, J. L., Boersma, E., Schotten, U., Schalij, M. J., … Allessie, M. A. (2010). Electropathological Substrate of Longstanding Persistent Atrial Fibrillation in Patients With Structural Heart Disease. Circulation, 122(17), 1674-1682. doi:10.1161/circulationaha.109.910901Earley, M. J., Abrams, D. J. R., Sporton, S. C., & Schilling, R. J. (2006). Validation of the Noncontact Mapping System in the Left Atrium During Permanent Atrial Fibrillation and Sinus Rhythm. Journal of the American College of Cardiology, 48(3), 485-491. doi:10.1016/j.jacc.2006.04.069Gianni, C., Mohanty, S., Di Biase, L., Metz, T., Trivedi, C., Gökoğlan, Y., … Natale, A. (2016). Acute and early outcomes of focal impulse and rotor modulation (FIRM)-guided rotors-only ablation in patients with nonparoxysmal atrial fibrillation. Heart Rhythm, 13(4), 830-835. doi:10.1016/j.hrthm.2015.12.028GOJRATY, S., LAVI, N., VALLES, E., KIM, S. J., MICHELE, J., & GERSTENFELD, E. P. (2009). Dominant Frequency Mapping of Atrial Fibrillation: Comparison of Contact and Noncontact Approaches. Journal of Cardiovascular Electrophysiology, 20(9), 997-1004. doi:10.1111/j.1540-8167.2009.01488.xGrandi, E., Pandit, S. V., Voigt, N., Workman, A. J., Dobrev, D., Jalife, J., & Bers, D. M. (2011). Human Atrial Action Potential and Ca 2+ Model. Circulation Research, 109(9), 1055-1066. doi:10.1161/circresaha.111.253955Gray, R. A., Pertsov, A. M., & Jalife, J. (1998). Spatial and temporal organization during cardiac fibrillation. Nature, 392(6671), 75-78. doi:10.1038/32164Guillem, M. S., Climent, A. M., Millet, J., Arenal, Á., Fernández-Avilés, F., Jalife, J., … Berenfeld, O. (2013). Noninvasive Localization of Maximal Frequency Sites of Atrial Fibrillation by Body Surface Potential Mapping. Circulation: Arrhythmia and Electrophysiology, 6(2), 294-301. doi:10.1161/circep.112.000167Guillem, M. S., Climent, A. M., Rodrigo, M., Fernández-Avilés, F., Atienza, F., & Berenfeld, O. (2016). Presence and stability of rotors in atrial fibrillation: evidence and therapeutic implications. Cardiovascular Research, 109(4), 480-492. doi:10.1093/cvr/cvw011Gurevich, D. R., & Grigoriev, R. O. (2019). Robust approach for rotor mapping in cardiac tissue. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(5), 053101. doi:10.1063/1.5086936HAISSAGUERRE, M., HOCINI, M., SHAH, A. J., DERVAL, N., SACHER, F., JAIS, P., & DUBOIS, R. (2013). Noninvasive Panoramic Mapping of Human Atrial Fibrillation Mechanisms: A Feasibility Report. Journal of Cardiovascular Electrophysiology, 24(6), 711-717. doi:10.1111/jce.12075Iyer, A. N., & Gray, R. A. (2001). An Experimentalist’s Approach to Accurate Localization of Phase Singularities during Reentry. Annals of Biomedical Engineering, 29(1), 47-59. doi:10.1114/1.1335538Jalife, J. (2002). Mother rotors and fibrillatory conduction: a mechanism of atrial fibrillation. Cardiovascular Research, 54(2), 204-216. doi:10.1016/s0008-6363(02)00223-7Jalife, J., Filgueiras Rama, D., & Berenfeld, O. (2015). Letter by Jalife et al Regarding Article, «Quantitative Analysis of Localized Sources Identified by Focal Impulse and Rotor Modulation Mapping in Atrial Fibrillation». Circulation: Arrhythmia and Electrophysiology, 8(5), 1296-1298. doi:10.1161/circep.115.003324Jarman, J. W. E., Wong, T., Kojodjojo, P., Spohr, H., Davies, J. E., Roughton, M., … Peters, N. S. (2012). Spatiotemporal Behavior of High Dominant Frequency During Paroxysmal and Persistent Atrial Fibrillation in the Human Left Atrium. Circulation: Arrhythmia and Electrophysiology, 5(4), 650-658. doi:10.1161/circep.111.967992Kuklik, P., Zeemering, S., Maesen, B., Maessen, J., Crijns, H. J., Verheule, S., … Schotten, U. (2015). Reconstruction of Instantaneous Phase of Unipolar Atrial Contact Electrogram Using a Concept of Sinusoidal Recomposition and Hilbert Transform. IEEE Transactions on Biomedical Engineering, 62(1), 296-302. doi:10.1109/tbme.2014.2350029Identification of Rotors during Human Atrial Fibrillation Using Contact Mapping and Phase Singularity Detection: Technical Considerations. (2017). IEEE Transactions on Biomedical Engineering, 64(2), 310-318. doi:10.1109/tbme.2016.2554660Lee, Y.-S., Song, J.-S., Hwang, M., Lim, B., Joung, B., & Pak, H.-N. (2016). A New Efficient Method for Detecting Phase Singularity in Cardiac Fibrillation. PLOS ONE, 11(12), e0167567. doi:10.1371/journal.pone.0167567Li, X., Chu, G. S., Almeida, T. P., Salinet, J. L., Dastagir, N., Mistry, A. R., … André Ng, G. (2017). 5Characteristics of ablated rotors in terminating persistent atrial fibrillation using non-contact mapping. EP Europace, 19(suppl_1), i3-i3. doi:10.1093/europace/eux283.145Li, X., Salinet, J. L., Almeida, T. P., Vanheusden, F. J., Chu, G. S., Ng, G. A., & Schlindwein, F. S. (2017). An interactive platform to guide catheter ablation in human persistent atrial fibrillation using dominant frequency, organization and phase mapping. Computer Methods and Programs in Biomedicine, 141, 83-92. doi:10.1016/j.cmpb.2017.01.011Mandapati, R., Skanes, A., Chen, J., Berenfeld, O., & Jalife, J. (2000). Stable Microreentrant Sources as a Mechanism of Atrial Fibrillation in the Isolated Sheep Heart. Circulation, 101(2), 194-199. doi:10.1161/01.cir.101.2.194Narayan, S. M., Baykaner, T., Clopton, P., Schricker, A., Lalani, G. G., Krummen, D. E., … Miller, J. M. (2014). Ablation of Rotor and Focal Sources Reduces Late Recurrence of Atrial Fibrillation Compared With Trigger Ablation Alone. Journal of the American College of Cardiology, 63(17), 1761-1768. doi:10.1016/j.jacc.2014.02.543NARAYAN, S. M., KRUMMEN, D. E., & RAPPEL, W.-J. (2012). Clinical Mapping Approach To Diagnose Electrical Rotors and Focal Impulse Sources for Human Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 23(5), 447-454. doi:10.1111/j.1540-8167.2012.02332.xNarayan, S. M., Krummen, D. E., Shivkumar, K., Clopton, P., Rappel, W.-J., & Miller, J. M. (2012). Treatment of Atrial Fibrillation by the Ablation of Localized Sources. Journal of the American College of Cardiology, 60(7), 628-636. doi:10.1016/j.jacc.2012.05.022Nattel, S. (2002). New ideas about atrial fibrillation 50 years on. Nature, 415(6868), 219-226. doi:10.1038/415219aNattel, S. (2003). Atrial Electrophysiology and Mechanisms of Atrial Fibrillation. Journal of Cardiovascular Pharmacology and Therapeutics, 8(1_suppl), S5-S11. doi:10.1177/107424840300800102Ortigosa, N., Fernández, C., Galbis, A., & Cano, Ó. (2015). Phase information of time-frequency transforms as a key feature for classification of atrial fibrillation episodes. Physiological Measurement, 36(3), 409-424. doi:10.1088/0967-3334/36/3/409Pandit, S. V., & Jalife, J. (2013). Rotors and the Dynamics of Cardiac Fibrillation. Circulation Research, 112(5), 849-862. doi:10.1161/circresaha.111.300158VII. Mathematical contributions to the theory of evolution.—III. Regression, heredity, and panmixia. (1896). Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 187, 253-318. doi:10.1098/rsta.1896.0007Pertsov, A. M., Davidenko, J. M., Salomonsz, R., Baxter, W. T., & Jalife, J. (1993). Spiral waves of excitation underlie reentrant activity in isolated cardiac muscle. Circulation Research, 72(3), 631-650. doi:10.1161/01.res.72.3.631Podziemski, P., Zeemering, S., Kuklik, P., van Hunnik, A., Maesen, B., Maessen, J., … Schotten, U. (2018). Rotors Detected by Phase Analysis of Filtered, Epicardial Atrial Fibrillation Electrograms Colocalize With Regions of Conduction Block. Circulation: Arrhythmia and Electrophysiology, 11(10). doi:10.1161/circep.117.005858Wieser, L., Stühlinger, M. C., Hintringer, F., Tilg, B., Fischer, G., & Rantner, L. J. (2007). Detection of Phase Singularities in Triangular Meshes. Methods of Information in Medicine, 46(06), 646-654. doi:10.3414/me0427Ríos-Muñoz, G. R., Arenal, Á., & Artés-Rodríguez, A. (2018). Real-Time Rotational Activity Detection in Atrial Fibrillation. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.00208Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms. Circulation: Arrhythmia and Electrophysiology, 10(9). doi:10.1161/circep.117.005008Rodrigo, M., Guillem, M. S., Climent, A. M., Pedrón-Torrecilla, J., Liberos, A., Millet, J., … Berenfeld, O. (2014). Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study. Heart Rhythm, 11(9), 1584-1591. doi:10.1016/j.hrthm.2014.05.013Roney, C. H., Cantwell, C. D., Bayer, J. D., Qureshi, N. A., Lim, P. B., Tweedy, J. H., … Ng, F. S. (2017). Spatial Resolution Requirements for Accurate Identification of Drivers of Atrial Fibrillation. Circulation: Arrhythmia and Electrophysiology, 10(5). doi:10.1161/circep.116.004899Salinet, J., Schlindwein, F. S., Stafford, P., Almeida, T. P., Li, X., Vanheusden, F. J., … Ng, G. A. (2017). Propagation of meandering rotors surrounded by areas of high dominant frequency in persistent atrial fibrillation. Heart Rhythm, 14(9), 1269-1278. doi:10.1016/j.hrthm.2017.04.031Salinet, J. L., Madeiro, J. P. V., Cortez, P. C., Stafford, P. J., André Ng, G., & Schlindwein, F. S. (2013). Analysis of QRS-T subtraction in unipolar atrial fibrillation electrograms. Medical & Biological Engineering & Computing, 51(12), 1381-1391. doi:10.1007/s11517-013-1071-4Salinet, J. L., Oliveira, G. N., Vanheusden, F. J., Comba, J. L. D., Ng, G. A., & Schlindwein, F. S. (2013). Visualizing intracardiac atrial fibrillation electrograms using spectral analysis. Computing in Science & Engineering, 15(2), 79-87. doi:10.1109/mcse.2013.37Schilling, R. J., Peters, N. S., & Davies, D. W. (1998). Simultaneous Endocardial Mapping in the Human Left Ventricle Using a Noncontact Catheter. Circulation, 98(9), 887-898. doi:10.1161/01.cir.98.9.887Schricker, A. A., Lalani, G. G., Krummen, D. E., & Narayan, S. M. (2014). Rotors as Drivers of Atrial Fibrillation and Targets for Ablation. Current Cardiology Reports, 16(8). doi:10.1007/s11886-014-0509-0Steinberg, J. S., Shah, Y., Bhatt, A., Sichrovsky, T., Arshad, A., Hansinger, E., & Musat, D. (2017). Focal impulse and rotor modulation: Acute procedural observations and extended clinical follow-up. Heart Rhythm, 14(2), 192-197. doi:10.1016/j.hrthm.2016.11.008THIAGALINGAM, A., WALLACE, E. M., BOYD, A. C., EIPPER, V. E., CAMPBELL, C. R., BYTH, K., … KOVOOR, P. (2004). Noncontact Mapping of the Left Ventricle:. Insights from Validation with Transmural Contact Mapping. Pacing and Clinical Electrophysiology, 27(5), 570-578. doi:10.1111/j.1540-8159.2004.00489.xUmapathy, K., Nair, K., Masse, S., Krishnan, S., Rogers, J., Nash, M. P., & Nanthakumar, K. (2010). Phase Mapping of Cardiac Fibrillation. Circulation: Arrhythmia and Electrophysiology, 3(1), 105-114. doi:10.1161/circep.110.853804WITTKAMPF, F. H. M., & NAKAGAWA, H. (2006). RF Catheter Ablation: Lessons on Lesions. Pacing and Clinical Electrophysiology, 29(11), 1285-1297. doi:10.1111/j.1540-8159.2006.00533.xWang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/tip.2003.81986