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Search results for: QUADRATIC STOCHASTIC OPERATORS
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Stochastic intervals for the family of quadratic maps
Open Research DataNumerical analysis of chaotic dynamics is a challenging task. The one-parameter families of logistic maps and closely related quadratic maps f_a(x)=a-x^2 are well-known examples of such dynamical systems. Determining parameter values that yield stochastic-like dynamics is especially difficult, because although this set has positive Lebesgue measure,...
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Uniform expansion estimates in the quadratic map as a function of the critical neighborhood size
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the parameter, with a large range of parameters
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the parameter, with a small critical neighborhood
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the parameter, with a very small critical neighborhood
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the partition size, using Johnson’s algorithm
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the partition size, computing λ only
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the parameter, using the “uniform” partition type
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the parameter, using the “critical” partition type
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the parameter, using the “derivative” partition type
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the partition size, using the “derivative” partition type
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the partition size, using the “critical” partition type
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map as a function of the partition size, using the Floyd–Warshall algorithm
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map with the smallest critical neighborhood for which the expansion exponent λ0 is positive
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map with the smallest critical neighborhood for which the expansion exponent λ is positive
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map with the smallest critical neighborhood for which the expansion exponent λ0 is greater than 0.1
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Uniform expansion estimates in the quadratic map with the smallest critical neighborhood for which the expansion exponent λ is greater than 0.1
Open Research DataThis dataset contains selected results of numerical computations described in the paper "Quantitative hyperbolicity estimates in one-dimensional dynamics" by S. Day, H. Kokubu, S. Luzzatto, K. Mischaikow, H. Oka, P. Pilarczyk, published in Nonlinearity, Vol. 21, No. 9 (2008), 1967-1987, doi: 10.1088/0951-7715/21/9/002.
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Top 10 Iran Export Partners in 2011
Open Research DataLifting of Iranian sanctions could lead to a rapid improvement in the economic situation of Iran. This will not only remove the barriers of international transactions, but also enable the investments of foreign entities on the territory of Iran, increasing the competitiveness of operators, including in the key mining sector.
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Biofouling UF car wash
Open Research DataPolyethersulfone (PES) membranes were used to separate wastewater from car washes containing bacteria.After the separation was completed, the UF installations were washed with chemical agents.The study examined how effectively these chemical agents remove bacterial contamination from the installation.P3 Ultrasil 11 alkaline solutions (pH = 12) were...
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Harmonic Analysis
Open Research DataWe construct a decomposition of the identity operator on a Riemannian manifold M as a sum of smooth orthogonal projections subordinate to an open cover of M. This extends a decomposition on the real line by smooth orthogonal projection due to Coifman and Meyer (C. R. Acad. Sci. Paris, Sér. I Math., 312(3), 259–261 1991) and Auscher, Weiss, Wickerhauser...
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The aggregation of objects representing Gdańsk district buildings - scale 1:10000. Data from OSM.
Open Research DataThe process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the Open Street Map databases (OSM) [1].
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The aggregation of objects representing buildings in the Kartuzy district - scale 1:10000. Data from OSM.
Open Research DataThe process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the Open Street Map databases (OSM) [1].
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A collection of directed graphs for the minimum cycle mean weight computation
Open Research DataThis dataset contains definitions of the 16 directed graphs with weighted edges that were described in the following paper: Paweł Pilarczyk, A space-efficient algorithm for computing the minimum cycle mean in a directed graph, Journal of Mathematics and Computer Science, 20 (2020), no. 4, 349--355, DOI: 10.22436/jmcs.020.04.08, URL: http://dx.doi.org/10.22436/jmcs.020.04.08 These...
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The aggregation of objects representing Gdańsk district buildings - scale 1:10000
Open Research DataThe process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the national geodesy and cartography resource from BDOT10k (10k topographic database) [1].
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The aggregation of objects representing buildings in the Kartuzy district - scale 1:10000
Open Research DataThe process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the national geodesy and cartography resource from BDOT10k (10k topographic database) [1].
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The aggregation of objects representing Gdańsk district buildings - scale 1:25000
Open Research DataThe process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the national geodesy and cartography resource from BDOT10k (10k topographic database) [1].
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The aggregation of objects representing buildings in the Kartuzy district - scale 1:25000
Open Research DataThe process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the national geodesy and cartography resource from BDOT10k (10k topographic database) [1].
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The aggregation of objects representing Katowice district buildings - scale 1:25000
Open Research DataThe process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the national geodesy and cartography resource from BDOT10k (10k topographic database) [1].
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The aggregation of objects representing Katowice district buildings - scale 1:10000
Open Research DataThe process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the national geodesy and cartography resource from BDOT10k (10k topographic database) [1].
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AVHRR Level1CD covering Baltic Sea area year 2006
Open Research DataThe product level is the NOAA AVHRR Level 1C that is result of processing the AVHRR data from the HRPT stream based on ancillary information like sensing geometry and calibration data. Then converted into geophysical variables: top-of-the atmosphere (TOA) albedo or brightness temperature. Additionally, information like geolocation has been added. Other...
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AVHRR Level1CD covering Baltic Sea area year 2010
Open Research DataThe product level is the NOAA AVHRR Level 1C that is result of processing the AVHRR data from the HRPT stream based on ancillary information like sensing geometry and calibration data. Then converted into geophysical variables: top-of-the atmosphere (TOA) albedo or brightness temperature. Additionally, information like geolocation has been added. Other...
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AVHRR Level1CD covering Baltic Sea area year 2007
Open Research DataThe product level is the NOAA AVHRR Level 1C that is result of processing the AVHRR data from the HRPT stream based on ancillary information like sensing geometry and calibration data. Then converted into geophysical variables: top-of-the atmosphere (TOA) albedo or brightness temperature. Additionally, information like geolocation has been added. Other...
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AVHRR Level1CD covering Baltic Sea area year 2011
Open Research DataThe product level is the NOAA AVHRR Level 1C that is result of processing the AVHRR data from the HRPT stream based on ancillary information like sensing geometry and calibration data. Then converted into geophysical variables: top-of-the atmosphere (TOA) albedo or brightness temperature. Additionally, information like geolocation has been added. Other...
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AVHRR Level1CD covering Baltic Sea area year 2012
Open Research DataThe product level is the NOAA AVHRR Level 1C that is result of processing the AVHRR data from the HRPT stream based on ancillary information like sensing geometry and calibration data. Then converted into geophysical variables: top-of-the atmosphere (TOA) albedo or brightness temperature. Additionally, information like geolocation has been added. Other...
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AVHRR Level1CD covering Baltic Sea area year 2008
Open Research DataThe product level is the NOAA AVHRR Level 1C that is result of processing the AVHRR data from the HRPT stream based on ancillary information like sensing geometry and calibration data. Then converted into geophysical variables: top-of-the atmosphere (TOA) albedo or brightness temperature. Additionally, information like geolocation has been added. Other...
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AVHRR Level1CD covering Baltic Sea area year 2009
Open Research DataThe product level is the NOAA AVHRR Level 1C that is result of processing the AVHRR data from the HRPT stream based on ancillary information like sensing geometry and calibration data. Then converted into geophysical variables: top-of-the atmosphere (TOA) albedo or brightness temperature. Additionally, information like geolocation has been added. Other...
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AVHRR Level1CD covering Baltic Sea area year 2001
Open Research DataThe dataset contains data derived from recordings of the AVHRR/3 radiometer operating on board the NOAA POES (Polar Orbiting Environmental Satellites) Series - 5th Generation Satellites covering the Baltic Sea area. The satellite data was recorded in the years 2000-2012 directly by the HRPT station installed at the University of Gdańsk. The registration...
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AVHRR Level1CD covering Baltic Sea area year 2005
Open Research DataThe dataset contains data derived from recordings of the AVHRR/3 radiometer operating on board the NOAA POES (Polar Orbiting Environmental Satellites) Series - 5th Generation Satellites covering the Baltic Sea area. The satellite data was recorded in the years 2000-2012 directly by the HRPT station installed at the University of Gdańsk. The registration...
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AVHRR Level1CD covering Baltic Sea area year 2004
Open Research DataThe dataset contains data derived from recordings of the AVHRR/3 radiometer operating on board the NOAA POES (Polar Orbiting Environmental Satellites) Series - 5th Generation Satellites covering the Baltic Sea area. The satellite data was recorded in the years 2000-2012 directly by the HRPT station installed at the University of Gdańsk. The registration...
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AVHRR Level1CD covering Baltic Sea area year 2003
Open Research DataThe dataset contains data derived from recordings of the AVHRR/3 radiometer operating on board the NOAA POES (Polar Orbiting Environmental Satellites) Series - 5th Generation Satellites covering the Baltic Sea area. The satellite data was recorded in the years 2000-2012 directly by the HRPT station installed at the University of Gdańsk. The registration...
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AVHRR Level1CD covering Baltic Sea area year 2002
Open Research DataThe dataset contains data derived from recordings of the AVHRR/3 radiometer operating on board the NOAA POES (Polar Orbiting Environmental Satellites) Series - 5th Generation Satellites covering the Baltic Sea area. The satellite data was recorded in the years 2000-2012 directly by the HRPT station installed at the University of Gdańsk. The registration...