621 research outputs found
Intrapopulational chromosome number variation in Zephyranthes sylvatica Baker (Amaryllidaceae: Hippeastreae) from northeast Brazil.
Zephyranthes Herb. is a taxonomically complex and cytologically variable group, withabout 65 species of Neotropical distribution. Chromosome number variability in 32 individuals of a Zephyranthes sylvaticapopulation from Northeast Brazil was investigated. Three cytotypes were found: 2n = 12 (one metacentric, four submetacentricand one acrocentric pairs), in 24 individuals; 2n = 12 + 1B, in five and three individuals with 2n = 18, a triploid cytotype.All diploid individuals showed chromosomes with polymorphism in pair one and two, while in triploids this polymorphismwas observed in all chromosome triplets, generally with two homomorphic chromosomes and a higher or lower heteromorphicchromosome. All individuals had reticulated interfasic nucleus and a slightly asymmetric chromosome complement, withone metacentric chromosome pair and the others more submetacentric to acrocentric. These data confirm the cytologicalvariability previously registered for the genus. Mechanisms involved in karyotypic evolution in this population are discussed
Geometry and quantum delocalization of interstitial oxygen in silicon
The problem of the geometry of interstitial oxygen in silicon is settled by
proper consideration of the quantum delocalization of the oxygen atom around
the bond-center position. The calculated infrared absorption spectrum accounts
for the 517 and 1136 cm bands in their position, character, and isotope
shifts. The asymmetric lineshape of the 517 cm peak is also well
reproduced. A new, non-infrared-active, symmetric-stretching mode is found at
596 cm. First-principles calculations are presented supporting the
nontrivial quantum delocalization of the oxygen atom.Comment: uuencoded, compressed postscript file for the whole. 4 pages (figures
included), accepted in PR
Production and characterization of recombinant frutalin expressed in the yeast Pichia Pastoris
CNPq, CAPES, FUNCAP, FCT, ALBAN, UMINHO, UFC, UNIFO
Gymnosperms on the EDGE
Driven by limited resources and a sense of urgency, the prioritization of species for conservation has been a persistent concern in conservation science. Gymnosperms (comprising ginkgo, conifers, cycads, and gnetophytes) are one of the most threatened groups of living organisms, with 40% of the species at high risk of extinction, about twice as many as the most recent estimates for all plants (i.e. 21.4%). This high proportion of species facing extinction highlights the urgent action required to secure their future through an objective prioritization approach. The Evolutionary Distinct and Globally Endangered (EDGE) method rapidly ranks species based on their evolutionary distinctiveness and the extinction risks they face. EDGE is applied to gymnosperms using a phylogenetic tree comprising DNA sequence data for 85% of gymnosperm species (923 out of 1090 species), to which the 167 missing species were added, and IUCN Red List assessments available for 92% of species. The effect of different extinction probability transformations and the handling of IUCN data deficient species on the resulting rankings is investigated. Although top entries in our ranking comprise species that were expected to score well (e.g. Wollemia nobilis, Ginkgo biloba), many were unexpected (e.g. Araucaria araucana). These results highlight the necessity of using approaches that integrate evolutionary information in conservation science
Empresa MASAME
En el siguiente documento se presenta el trabajo realizado en el Proyecto de AplicaciĂłn Profesional ConsultorĂa para la Competitividad (PAP CC) en el cual se intervino a la empresa, basĂĄndonos en el diagnĂłstico que realizĂł el equipo de
primavera 2022 nos dimos cuenta de que habĂa 3 ĂĄreas de mejora importante, estrategia, gestiĂłn de mercado y sistemas de calidad.
Por cuestiones de tiempo, decidimos enfocarnos en una sola ĂĄrea para entregar resultados completos y de calidad. Tras las visitas a la empresa y el anĂĄlisis previo nos dimos cuenta de que la empresa no podrĂa mejorar su gestiĂłn de mercado y los
sistemas de calidad si no contaban con una estrategia bien definida, por lo que se tomĂł la decisiĂłn de definir y mejorar la estrategia de esta.
Uno de los productos para la empresa fue la propuesta de un organigrama, con la finalidad de que puedan ver lo que ya tienen y tambiĂ©n considerar que es lo que les falta para cubrir todas las necesidades de la empresa. La segunda propuesta realizada fue una visiĂłn y una misiĂłn mĂĄs profunda y completa. Estos aspectos son los que le dan una razĂłn de ser a la empresa y una razĂłn de estar a los empleados por lo que tienen que inspirar y guiar. TambiĂ©n se trabajĂł en desarrollar el Business Model Canvas el cual plasma de manera visual la idea, los componentes y los participantes fundamentales de la empresa. Por Ășltimo, se realizĂł un formato financiero que proporciona indicadores para ayudar en la toma de decisiones.ITESO, A.C
Heralded quantum entanglement between two crystals
Quantum networks require the crucial ability to entangle quantum nodes. A
prominent example is the quantum repeater which allows overcoming the distance
barrier of direct transmission of single photons, provided remote quantum
memories can be entangled in a heralded fashion. Here we report the observation
of heralded entanglement between two ensembles of rare-earth-ions doped into
separate crystals. A heralded single photon is sent through a 50/50
beamsplitter, creating a single-photon entangled state delocalized between two
spatial modes. The quantum state of each mode is subsequently mapped onto a
crystal, leading to an entangled state consisting of a single collective
excitation delocalized between two crystals. This entanglement is revealed by
mapping it back to optical modes and by estimating the concurrence of the
retrieved light state. Our results highlight the potential of rare-earth-ions
doped crystals for entangled quantum nodes and bring quantum networks based on
solid-state resources one step closer.Comment: 10 pages, 5 figure
The stable free rank of symmetry of products of spheres
A well known conjecture in the theory of transformation groups states that if
p is a prime and (Z/p)^r acts freely on a product of k spheres, then r is less
than or equal to k. We prove this assertion if p is large compared to the
dimension of the product of spheres. The argument builds on tame homotopy
theory for non simply connected spaces.Comment: 30 pages; improved exposition, some details adde
Forced Solid-State Interactions for the Selective âTurn-Onâ Fluorescence Sensing of Aluminum Ions in Water Using a Sensory Polymer Substrate
Selective and sensitive solid sensory substrates for detecting Al(III) in pure
water are reported. The material is a flexible polymer film that can be handled and exhibits gel
behavior and membrane performance. The film features a chemically anchored salicylaldehyde
benzoylhydrazone derivative as an aluminum ion fluorescence sensor. A novel procedure for
measuring Al(III) at the ppb level using a single solution drop in 20 min was developed. In this
procedure, a drop was allowed to enter the hydrophilic material for 15 min before a 5 min
drying period. The process forced the Al(III) to interact with the sensory motifs within the
membrane before measuring the fluorescence of the system. The limit of detection of Al(III)
was 22 ppm. Furthermore, a water-soluble sensory polymer containing the same sensory
motifs was developed with a limit of detection of Al(III) of 1.5 ppb, which was significantly
lower than the Environmental Protection Agency recommendations for drinking water.Spanish Ministerio de EconomiÌa
y Competitividad-Feder
(MAT2011-22544) and by the ConsejeriÌa
de EducacioÌn - Junta
de Castilla y LeoÌn (BU232U13)
A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images
[EN] This work describes a new hybrid method for accurate iris segmentation from full-face images independently of the ethnicity of the subject. It is based on a combination of three methods: facial key-point detection, integro-differential operator (IDO) and mathematical morphology. First, facial landmarks are extracted by means of the Chehra algorithm in order to obtain the eye location. Then, the IDO is applied to the extracted sub-image containing only the eye in order to locate the iris. Once the iris is located, a series of mathematical morphological operations is performed in order to accurately segment it. Results are obtained and compared among four different ethnicities (Asian, Black, Latino and White) as well as with two other iris segmentation algorithms. In addition, robustness against rotation, blurring and noise is also assessed. Our method obtains state-of-the-art performance and shows itself robust with small amounts of blur, noise and/or rotation. Furthermore, it is fast, accurate, and its code is publicly available.Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Diego-Mas, JA.; Alcañiz Raya, ML. (2019). A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images. EURASIP Journal on Image and Video Processing (Online). 2019(1):1-14. https://doi.org/10.1186/s13640-019-0473-0S11420191A. Radman, K. Jumari, N. Zainal, Fast and reliable iris segmentation algorithm. IET Image Process.7(1), 42â49 (2013).M. Erbilek, M. Fairhurst, M. C. D. C Abreu, in 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013). Age prediction from iris biometrics (London, 2013), pp. 1â5. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6913712&isnumber=6867223 .A. Abbasi, M. Khan, Iris-pupil thickness based method for determining age group of a person. Int. Arab J. Inf. Technol. (IAJIT). 13(6) (2016).G. Mabuza-Hocquet, F. Nelwamondo, T. Marwala, in Intelligent Information and Database Systems. ed. by N. Nguyen, S. Tojo, L. Nguyen, B. TrawiĆski. Ethnicity Distinctiveness Through Iris Texture Features Using Gabor Filters. ACIIDS 2017. Lecture Notes in Computer Science, vol. 10192 (Springer, Cham, 2017).S. Lagree, K. W. Bowyer, in 2011 IEEE International Conference on Technologies for Homeland Security (HST). Predicting ethnicity and gender from iris texture (IEEEWaltham, 2011). p. 440â445. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6107909&isnumber=6107829 .J. G. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell.15(11), 1148â1161 (1993).N. Kourkoumelis, M. Tzaphlidou. Medical Safety Issues Concerning the Use of Incoherent Infrared Light in Biometrics, eds. A. Kumar, D. Zhang. Ethics and Policy of Biometrics. ICEB 2010. Lecture Notes in Computer Science, vol 6005 (Springer, Berlin, Heidelberg, 2010).R. P. Wildes, Iris recognition: an emerging biometric technology. Proc. IEEE. 85(9), 1348â1363 (1997).M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models. Int. J. Comput. Vision. 1(4), 321â331 (1988).S. J. Pundlik, D. L. Woodard, S. T. Birchfield, in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Non-ideal iris segmentation using graph cuts (IEEEAnchorage, 2008). p. 1â6. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4563108&isnumber=4562948 .H. Proença, Iris recognition: On the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell.32(8), 1502â1516 (2010). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5156505&isnumber=5487331 .T. Tan, Z. He, Z. Sun, Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vision Comput.28(2), 223â230 (2010).C. -W. Tan, A. Kumar, in CVPR 2011 WORKSHOPS. Automated segmentation of iris images using visible wavelength face images (Colorado Springs, 2011). p. 9â14. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981682&isnumber=5981671 .Y. -H. Li, M. Savvides, An automatic iris occlusion estimation method based on high-dimensional density estimation. IEEE Trans. Pattern Anal. Mach. Intell.35(4), 784â796 (2013).M. Yahiaoui, E. Monfrini, B. Dorizzi, Markov chains for unsupervised segmentation of degraded nir iris images for person recognition. Pattern Recogn. Lett.82:, 116â123 (2016).A. Radman, N. Zainal, S. A. Suandi, Automated segmentation of iris images acquired in an unconstrained environment using hog-svm and growcut. Digit. Signal Proc.64:, 60â70 (2017).N. Liu, H. Li, M. Zhang, J. Liu, Z. Sun, T. Tan, in 2016 International Conference on Biometrics (ICB). Accurate iris segmentation in non-cooperative environments using fully convolutional networks (Halmstad, 2016). p. 1â8. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7550055&isnumber=7550036 .Z. Zhao, A. Kumar, in 2017 IEEE International Conference on Computer Vision (ICCV). Towards more accurate iris recognition using deeply learned spatially corresponding features (Venice, 2017). p. 3829â3838. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237673&isnumber=8237262 .P. Li, X. Liu, L. Xiao, Q. Song, Robust and accurate iris segmentation in very noisy iris images. Image Vision Comput.28(2), 246â253 (2010).D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D. -K. Park, J. Kim, A new iris segmentation method for non-ideal iris images. Image Vision Comput.28(2), 254â260 (2010).Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, J. Andrian, A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vision Comput. 28(2), 261â269 (2010).Z. Zhao, A. Kumar, in 2015 IEEE International Conference on Computer Vision (ICCV). An accurate iris segmentation framework under relaxed imaging constraints using total variation model (Santiago, 2015). p. 3828â3836. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7410793&isnumber=7410356 .Y. Hu, K. Sirlantzis, G. Howells, Improving colour iris segmentation using a model selection technique. Pattern Recogn. Lett.57:, 24â32 (2015).E. Ouabida, A. Essadique, A. Bouzid, Vander lugt correlator based active contours for iris segmentation and tracking. Expert Systems Appl.71:, 383â395 (2017).C. -W. Tan, A. Kumar, Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans. Image Proc.21(9), 4068â4079 (2012).C. -W. Tan, A. Kumar, in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). Human identification from at-a-distance images by simultaneously exploiting iris and periocular features (Tsukuba, 2012). p. 553â556. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460194&isnumber=6460043 .C. -W. Tan, A. Kumar, Towards online iris and periocular recognition under relaxed imaging constraints. IEEE Trans. Image Proc.22(10), 3751â3765 (2013).K. Y. Shin, Y. G. Kim, K. R. Park, Enhanced iris recognition method based on multi-unit iris images. Opt. Eng.52(4), 047201â047201 (2013).CASIA iris databases. http://biometrics.idealtest.org/ . Accessed 06 Sept 2017.WVU iris databases. hhttp://biic.wvu.edu/data-sets/synthetic-iris-dataset . Accessed 06 Sept 2017.UBIRIS iris database. http://iris.di.ubi.pt . Accessed 06 Sept 2017.MICHE iris database. http://biplab.unisa.it/MICHE/ . Accessed 06 Sept 2017.P. J. Phillips, et al, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRâ05), 1. Overview of the face recognition grand challenge (San Diego, 2005). p. 947â954. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467368&isnumber=31472 .D. S. Ma, J. Correll, B. Wittenbrink, The chicago face database: A free stimulus set of faces and norming data. Behav. Res. Methods. 47(4), 1122â1135 (2015).P. Soille, Morphological Image Analysis: Principles and Applications (Springer, 2013).A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, Inc., Englewood Cliffs, 1989).J. Daugman, How iris recognition works. IEEE Trans. Circ. Syst. Video Technol.14(1), 21â30 (2004).A. Asthana, S. Zafeiriou, S. Cheng, M. Pantic, in 2014 IEEE Conference on Computer Vision and Pattern Recognition. Incremental face alignment in the wild (Columbus, 2014). p. 1859â1866. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909636&isnumber=6909393 .T. Baltrusaitis, P. Robinson, L. -P. Morency, in 2013 IEEE International Conference on Computer Vision Workshops. Constrained local neural fields for robust facial landmark detection in the wild (Sydney, 2013). p. 354â361. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6755919&isnumber=6755862 .X. Zhu, D. Ramanan, in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference On. Face detection, pose estimation, and landmark localization in the wild (IEEEBerlin Heidelberg, 2012), pp. 2879â2886.G. Tzimiropoulos, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Project-out cascaded regression with an application to face alignment (Boston, 2015). p. 3659â3667. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298989&isnumber=7298593 .H. Hofbauer, F. Alonso-Fernandez, P. Wild, J. Bigun, A. Uhl, in 2014 22nd International Conference on Pattern Recognition. A ground truth for iris segmentation (Stockholm, 2014). p. 527â532. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6976811&isnumber=6976709 .H. Proença, L. A. Alexandre, in 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems. The NICE.I: Noisy Iris Challenge Evaluation - Part I (Crystal City, 2007). p. 1â4. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401910&isnumber=4401902 .J. Daugman, in European Convention on Security and Detection. High confidence recognition of persons by rapid video analysis of iris texture, (1995). p. 244â251. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=491729&isnumber=10615 .Code of Matlab implementation of Daugmanâs integro-differential operator (IDO). https://es.mathworks.com/matlabcentral/fileexchange/15652-iris-segmentation-using-daugman-s-integrodifferential-operator/ . Accessed 06 Sept 2017.Code of Matlab implementation of Zhao and Kumarâs iris segmentation framework under relaxed imaging constraints using total variation model. http://www4.comp.polyu.edu.hk/~csajaykr/tvmiris.htm/ . Accessed 06 Sept 2017.Code of Matlab implementation of presented work. https://gitlab.com/ffuentes/hybrid_iris_segmentation/ . Accessed 06 Sept 2017.Face and eye detection with OpenCV. https://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html . Accessed 07 Sept 2018.A. K. Boyat, B. K. Joshi, 6. A review paper:noise models in digital image processing signal & image processing. An International Journal (SIPIJ), (2015), pp. 63â75. https://doi.org/10.5121/sipij.2015.6206 .A. Buades, Y. Lou, J. M. Morel, Z. Tang, Multi image noise estimation and denoising (2010). Available: https://hal.archives-ouvertes.fr/hal-00510866/
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