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Journal articles on the topic 'Data / knowledge partitioning and distribution'

Author: Grafiati

Published: 4 May 2024

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1

Rota, Jadranka, Tobias Malm, Nicolas Chazot, Carlos Peña, and Niklas Wahlberg. "A simple method for data partitioning based on relative evolutionary rates." PeerJ 6 (August 28, 2018): e5498. http://dx.doi.org/10.7717/peerj.5498.

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Background Multiple studies have demonstrated that partitioning of molecular datasets is important in model-based phylogenetic analyses. Commonly, partitioning is done a priori based on some known properties of sequence evolution, e.g. differences in rate of evolution among codon positions of a protein-coding gene. Here we propose a new method for data partitioning based on relative evolutionary rates of the sites in the alignment of the dataset being analysed. The rates are inferred using the previously published Tree Independent Generation of Evolutionary Rates (TIGER), and the partitioning

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Shaikh, M. Bilal, M. Abdul Rehman, and Attaullah Sahito. "Optimizing Distributed Machine Learning for Large Scale EEG Data Set." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 114. http://dx.doi.org/10.30537/sjcms.v1i1.14.

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Distributed Machine Learning (DML) has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined int

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Liu, Richen, Liming Shen, Xueyi Chen, et al. "Sketch-Based Slice Interpretative Visualization for Stratigraphic Data." Journal of Imaging Science and Technology 63, no. 6 (2019): 60505–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2019.63.6.060505.

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Abstract In this article, the authors propose a stratigraphic slice interpretative visualization system, namely slice analyzer. It enables the domain experts, i.e., geologists and oil/gas exploration experts, to interactively interpret the slices with domain knowledge, which helps them get a better understanding of stratigraphic structures and the distribution of the geological materials, e.g., underground flow path (UFP), river delta, floodplain, slump fan, etc. In addition to some domain-specific slice edit manipulations, a sketch-based sub-region partitioning approach is further presented t

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Zhu, Zichen, Xiao Hu, and Manos Athanassoulis. "NOCAP: Near-Optimal Correlation-Aware Partitioning Joins." Proceedings of the ACM on Management of Data 1, no. 4 (2023): 1–27. http://dx.doi.org/10.1145/3626739.

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Storage-based joins are still commonly used today because the memory budget does not always scale with the data size. One of the many join algorithms developed that has been widely deployed and proven to be efficient is the Hybrid Hash Join (HHJ), which is designed to exploit any available memory to maximize the data that is joined directly in memory. However, HHJ cannot fully exploit detailed knowledge of the join attribute correlation distribution. In this paper, we show that given a correlation skew in the join attributes, HHJ partitions data in a suboptimal way. To do that, we derive the o

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Sineglazov, Victor, Olena Chumachenko, and Eduard Heilyk. "Semi-controlled Learning in Information Processing Problems." Electronics and Control Systems 4, no. 70 (2022): 37–43. http://dx.doi.org/10.18372/1990-5548.70.16754.

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The article substantiates the need for further research of known methods and the development of new methods of machine learning – semi-supervized learning. It is shown that knowledge of the probability distribution density of the initial data obtained using unlabeled data should carry information useful for deriving the conditional probability distribution density of labels and input data. If this is not the case, semi-supervised learning will not provide any improvement over supervised learning. It may even happen that the use of unlabeled data reduces the accuracy of the prediction. For semi

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Sirbiladze, Gia, Bidzina Matsaberidze, Bezhan Ghvaberidze, Bidzina Midodashvili, and David Mikadze. "Fuzzy TOPSIS based selection index in the planning of emergency service facilities locations and goods transportation." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1949–62. http://dx.doi.org/10.3233/jifs-210636.

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The attributes influencing the decision-making process in planning transportation of goods from selected facilities locations in disaster zones are considered. Experts evaluate each candidate for humanitarian aid distribution centers (HADCs) (service centers) against each uncertainty factor in q-rung orthopair fuzzy sets (q-ROFS). For representation of experts’ knowledge in the input data for planning emergency service facilities locations a q-rung orthopair fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach is developed. Based on the offered fuzzy TOPSIS ag

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Smith, Bruce R., Christophe M. Herbinger, and Heather R. Merry. "Accurate Partition of Individuals Into Full-Sib Families From Genetic Data Without Parental Information." Genetics 158, no. 3 (2001): 1329–38. http://dx.doi.org/10.1093/genetics/158.3.1329.

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Abstract Two Markov chain Monte Carlo algorithms are proposed that allow the partitioning of individuals into full-sib groups using single-locus genetic marker data when no parental information is available. These algorithms present a method of moving through the sibship configuration space and locating the configuration that maximizes an overall score on the basis of pairwise likelihood ratios of being full-sib or unrelated or maximizes the full joint likelihood of the proposed family structure. Using these methods, up to 757 out of 759 Atlantic salmon were correctly classified into 12 full-s

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Grard, Aline, and Jean-François Deliège. "Characterizing Trace Metal Contamination and Partitioning in the Rivers and Sediments of Western Europe Watersheds." Hydrology 10, no. 2 (2023): 51. http://dx.doi.org/10.3390/hydrology10020051.

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Adsorption and desorption processes occurring on suspended and bed sediments were studied in two datasets from western Europe watersheds (Meuse and Mosel). Copper and zinc dissolved and total concentrations, total suspended sediment concentrations, mass concentrations, and grain sizes were analyzed. Four classes of mineral particle size were determined. Grain size distribution had to be considered in order to assess the trace metal particulate phase in the water column. The partitioning coefficients of trace metals between the dissolved and particulate phases were calculated. The objective of

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McDonald, H. Gregory. "Yukon to the Yucatan: Habitat partitioning in North American Late Pleistocene ground sloths (Xenarthra, Pilosa)." Journal of Palaeosciences 70, no. (1-2) (2021): 237–52. http://dx.doi.org/10.54991/jop.2021.17.

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The late Pleistocene mammalian fauna of North America included seven genera of ground sloth, representing four families. This cohort of megaherbivores had an extensive geographic range in North America from the Yukon in Canada to the Yucatan Peninsula in Mexico and inhabited a variety of biomes. Within this latitudinal range there are taxa with a distribution limited to temperate latitudes while others have a distribution restricted to tropical latitudes. Some taxa are better documented than others and more is known about their palaeoecology and habitat preferences, while our knowledge of the

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Dalton, Lori A., and Mohammadmahdi R. Yousefi. "Data Requirements for Model-Based Cancer Prognosis Prediction." Cancer Informatics 14s5 (January 2015): CIN.S30801. http://dx.doi.org/10.4137/cin.s30801.

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Cancer prognosis prediction is typically carried out without integrating scientific knowledge available on genomic pathways, the effect of drugs on cell dynamics, or modeling mutations in the population. Recent work addresses some of these problems by formulating an uncertainty class of Boolean regulatory models for abnormal gene regulation, assigning prognosis scores to each network based on intervention outcomes, and partitioning networks in the uncertainty class into prognosis classes based on these scores. For a new patient, the probability distribution of the prognosis class was evaluated

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Kerkweg, A., P. Jöckel, A. Pozzer, et al. "Consistent simulation of bromine chemistry from the marine boundary layer to the stratosphere, Part I: model description, sea salt aerosols and pH." Atmospheric Chemistry and Physics Discussions 8, no. 2 (2008): 7217–62. http://dx.doi.org/10.5194/acpd-8-7217-2008.

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Abstract. This is the first article of a series presenting a detailed analysis of bromine chemistry simulated with the atmospheric chemistry general circulation model ECHAM5/MESSy. Release from sea salt is an important bromine source, hence the model explicitly calculates aerosol chemistry and phase partitioning for coarse mode aerosol particles. Many processes including chemical reaction rates are influenced by the particle size distribution, and aerosol associated water strongly affects the aerosol pH. Knowledge of the aerosol pH is important as it determines the aerosol chemistry, e.g., the

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Kerkweg, A., P. Jöckel, A. Pozzer, et al. "Consistent simulation of bromine chemistry from the marine boundary layer to the stratosphere – Part 1: Model description, sea salt aerosols and pH." Atmospheric Chemistry and Physics 8, no. 19 (2008): 5899–917. http://dx.doi.org/10.5194/acp-8-5899-2008.

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Abstract. This is the first article of a series presenting a detailed analysis of bromine chemistry simulated with the atmospheric chemistry general circulation model ECHAM5/MESSy. Release from sea salt is an important bromine source, hence the model explicitly calculates aerosol chemistry and phase partitioning for coarse mode aerosol particles. Many processes including chemical reaction rates are influenced by the particle size distribution, and aerosol associated water strongly affects the aerosol pH. Knowledge of the aerosol pH is important as it determines the aerosol chemistry, e.g., the

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Molinié, Dylan, Kurosh Madani, Véronique Amarger, and Abdennasser Chebira. "Identifying the Regions of a Space with the Self-Parameterized Recursively Assessed Decomposition Algorithm (SPRADA)." Machine Learning and Knowledge Extraction 5, no. 3 (2023): 979–1009. http://dx.doi.org/10.3390/make5030051.

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This paper introduces a non-parametric methodology based on classical unsupervised clustering techniques to automatically identify the main regions of a space, without requiring the objective number of clusters, so as to identify the major regular states of unknown industrial systems. Indeed, useful knowledge on real industrial processes entails the identification of their regular states, and their historically encountered anomalies. Since both should form compact and salient groups of data, unsupervised clustering generally performs this task fairly accurately; however, this often requires th

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Benmouiza, Khalil. "NONLINEAR CLUSTERED ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM MODEL FOR HOURLY SOLAR IRRADIATION ESTIMATION." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 68, no. 1 (2023): 7–11. http://dx.doi.org/10.59277/rrst-ee.2023.68.1.1.

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Solar energy occupies an important place among the various sources of renewable energy. A precise knowledge of the distribution of solar irradiation in a specified location is needed before any solar irradiation system installation. This paper introduces a nonlinear clustering, adaptive-network-based fuzzy inference system (ANFIS) model to estimate the hourly solar irradiation data using meteorological inputs and clustering algorithms: grid partitioning, subtractive clustering, and fuzzy c-means. Comparing these clustering algorithms is investigated to classify the inputs into clusters, which

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Moura, Aloysio Souza de, Felipe Santana Machado, Ravi Fernandes Mariano, Cléber Rodrigo de Souza, Urica Carolina Lemos Mengez, and Marco Aurélio Leite Fontes. "Mesoscale bird distribution pattern in montane phytophysiognomies along an ecotone between two hotspots." Acta Scientiarum. Biological Sciences 43 (December 8, 2021): e56931. http://dx.doi.org/10.4025/actascibiolsci.v43i1.56931.

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Brazil has a high diversity of birds and presents the largest number of threatened bird species in the neotropical region. Even so, there are gaps in the bird knowledge, especially in ecotonal montane regions. Given this panorama, this study aimed to analyse the bird community distribution (richness, composition, and beta diversity between phytophysiognomies) of an ecotonal montane landscape of southeastern Brazil, with the purpose of detecting substitution patterns of bird species on a meso-scale. Using bird data performed during the years 1998 to 2015 in 46 sampling points, we found high bir

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Robinson, Kevin P., Duncan A. I. MacDougall, Connor C. G. Bamford, et al. "Ecological habitat partitioning and feeding specialisations of coastal minke whales (Balaenoptera acutorostrata) using a recently designated MPA in northeast Scotland." PLOS ONE 18, no. 7 (2023): e0246617. http://dx.doi.org/10.1371/journal.pone.0246617.

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In the design of protected areas for cetaceans, spatial maps rarely take account of the life-history and behaviour of protected species relevant to their spatial ambit, which may be important for their management. In this study, we examined the distribution and feeding behaviours of adult versus juvenile minke whales (Balaenoptera acutorostrata) from long-term studies in the Moray Firth in northeast Scotland, where a Marine Protected Area (MPA) has recently been designated. Data were collected during dedicated boat surveys between 2001 and 2022 inclusive, from which 784 encounters with 964 wha

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Wang, Ziyu, Kai Liu, Jingjing Cao, Liheng Peng, and Xin Wen. "Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine." Forests 13, no. 9 (2022): 1489. http://dx.doi.org/10.3390/f13091489.

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Mangroves are a key type of protected coastal wetland, with a range of benefits such as protection from wave damage, sand fixation, water purification and ecological tourism. As the academic knowledge of mangroves has gradually increased, the use of remote sensing to monitor their dynamic changes in China has become a hot topic of discussion and has received attention in academic circles. Remote sensing has also provided necessary auxiliary decision-making suggestions and data support for the scientific and rational conservation, restoration and management of mangrove resources. In this paper,

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Butler, Rebecca A., Mona Papeş, James T. Vogt, Dave J. Paulsen, Christopher Crowe, and Rebecca T. Trout Fryxell. "Human risk to tick encounters in the southeastern United States estimated with spatial distribution modeling." PLOS Neglected Tropical Diseases 18, no. 2 (2024): e0011919. http://dx.doi.org/10.1371/journal.pntd.0011919.

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Expanding geographic distribution and increased populations of ticks has resulted in an upsurge of human-tick encounters in the United States (US), leading to an increase in tickborne disease reporting. Limited knowledge of the broadscale spatial range of tick species is heightened by a rapidly changing environment. Therefore, we partnered with the Forest Inventory and Analysis (FIA) program of the Forest Service, U.S. Department of Agriculture and used passive tick surveillance to better understand spatiotemporal variables associated with foresters encountering three tick species (Amblyomma a

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Sánchez-Cabanes, Alicia, Maja Nimak-Wood, Nicola Harris, and Renaud De Stephanis. "Habitat preferences among three top predators inhabiting a degraded ecosystem, the Black Sea." Scientia Marina 81, no. 2 (2017): 217. http://dx.doi.org/10.3989/scimar.04493.07a.

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This study investigated whether there is evidence of widespread niche partitioning based on environmental factors in the Black Sea and tested the hypothesis that physiographic factors may be employed as predictors. It addresses poorly researched areas with good habitat potential for the only three cetacean subspecies living in this area: the Black Sea short-beaked common dolphin (Delphinus delphis spp. ponticus), the Black Sea bottlenose dolphin (Tursiops truncatus spp. ponticus) and the Black Sea harbour porpoise (Phocoena phocoena spp. relicta). Generalized additive models (GAMs) were used t

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Reyes-Palomeque, Gabriela, Juan Manuel Dupuy, Kristofer D. Johnson, Miguel Angel Castillo-Santiago, and J. Luis Hernández-Stefanoni. "Combining LiDAR data and airborne imagery of very high resolution to improve aboveground biomass estimates in tropical dry forests." Forestry: An International Journal of Forest Research 92, no. 5 (2019): 599–615. http://dx.doi.org/10.1093/forestry/cpz037.

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Abstract Knowledge of the spatial distribution of aboveground biomass (AGB) is crucial to guide forest conservation and management to maintain carbon stocks. LiDAR has been highly successful for this purpose, but has limited availability. Very-high resolution (&lt;1 m) orthophotos can also be used to estimate AGB because they allow a fine distinction of forest canopy grain. We evaluated the separate and joint performance of orthophotos and LiDAR data to estimate AGB in two types of tropical dry forests in the Yucatan Peninsula. Woody plants were surveyed in twenty 0.1 ha plots in a semidec

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Wen, Hang, Pamela L. Sullivan, Gwendolyn L. Macpherson, Sharon A. Billings, and Li Li. "Deepening roots can enhance carbonate weathering by amplifying CO<sub>2</sub>-rich recharge." Biogeosciences 18, no. 1 (2021): 55–75. http://dx.doi.org/10.5194/bg-18-55-2021.

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Abstract. Carbonate weathering is essential in regulating atmospheric CO2 and carbon cycle at the century timescale. Plant roots accelerate weathering by elevating soil CO2 via respiration. It however remains poorly understood how and how much rooting characteristics (e.g., depth and density distribution) modify flow paths and weathering. We address this knowledge gap using field data from and reactive transport numerical experiments at the Konza Prairie Biological Station (Konza), Kansas (USA), a site where woody encroachment into grasslands is surmised to deepen roots. Results indicate that

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GUILLER, ANNIE, ALAIN BELLIDO, ALAIN COUTELLE, and LUC MADEC. "Spatial genetic pattern in the land mollusc Helix aspersa inferred from a ‘centre-based clustering’ procedure." Genetical Research 88, no. 1 (2006): 27–44. http://dx.doi.org/10.1017/s0016672306008305.

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The present work provides the first broad-scale screening of allozymes in the land snail Helix aspersa. By using overall information available on the distribution of genetic variation between 102 populations previously investigated, we expect to strengthen our knowledge on the spread of the invasive aspersa subspecies in the Western Mediterranean. We propose a new approach based on a centre-based clustering procedure to cluster populations into groups following rules of geographical proximity and genetic similarity. Assuming a stepping-stone model of diffusion, we apply a partitioning algorith

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Islam, M. J., A. W. Reza, A. S. M. Z. Kausar, and H. Ramiah. "New Ray Tracing Method to Investigate the Various Effects on Wave Propagation in Medical Scenario: An Application of Wireless Body Area Network." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/306270.

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The advent of technology with the increasing use of wireless network has led to the development of Wireless Body Area Network (WBAN) to continuously monitor the change of physiological data in a cost efficient manner. As numerous researches on wave propagation characterization have been done in intrabody communication, this study has given emphasis on the wave propagation characterization between the control units (CUs) and wireless access point (AP) in a hospital scenario. Ray tracing is a tool to predict the rays to characterize the wave propagation. It takes huge simulation time, especially

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Velluet, C., J. Demarty, B. Cappelaere, et al. "Building a field- and model-based climatology of local water and energy cycles in the cultivated Sahel – annual budgets and seasonality." Hydrology and Earth System Sciences Discussions 11, no. 5 (2014): 4753–808. http://dx.doi.org/10.5194/hessd-11-4753-2014.

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Abstract. In the African Sahel, energy and water cycling at the land surface is pivotal for regional climate, water resources and land productivity, yet it is still extremely poorly documented. As a step towards a comprehensive climatological description of surface fluxes in this area, this study provides estimates of average annual budgets and seasonal cycles for two main land use types of the cultivated Sahelian belt, rainfed millet crop and fallow bush. These estimates build on the combination of a 7 year field dataset from two typical plots in southwestern Niger with detailed physically-ba

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Asyaev, Grigorii, Alexander Sokolov, and Alexey Ruchay. "Intelligent Algorithms for Event Processing and Decision Making on Information Protection Strategies against Cyberattacks." Mathematics 11, no. 18 (2023): 3939. http://dx.doi.org/10.3390/math11183939.

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This paper considers the main approaches to building algorithms for the decision support systems of information protection strategies against cyberattacks in the networks of automated process control systems (the so-called recommender systems). The advantages and disadvantages of each of the considered algorithms are revealed, and their applicability to the processing of the information security events of the UNSW-NB 15 dataset is analyzed. The dataset used contains raw network packets collected using the IXIA PerfectStorm software in the CyberRange laboratory of the Australian Cyber Security

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STRATHE, A. B., H. JØRGENSEN, E. KEBREAB, and A. DANFÆR. "Bayesian simultaneous equation models for the analysis of energy intake and partitioning in growing pigs." Journal of Agricultural Science 150, no. 6 (2012): 764–74. http://dx.doi.org/10.1017/s0021859612000275.

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SUMMARYThe objective of the current study was to develop Bayesian simultaneous equation models for modelling energy intake and partitioning in growing pigs. A key feature of the Bayesian approach is that parameters are assigned prior distributions, which may reflect the current state of nature. In the models, rates of metabolizable energy (ME) intake, protein deposition (PD) and lipid deposition (LD) were treated as dependent variables accounting for residuals being correlated. Two complementary equation systems were used to model ME intake (MEI), PD and LD. Informative priors were developed,

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Velluet, C., J. Demarty, B. Cappelaere, et al. "Building a field- and model-based climatology of local water and energy cycles in the cultivated Sahel – annual budgets and seasonality." Hydrology and Earth System Sciences 18, no. 12 (2014): 5001–24. http://dx.doi.org/10.5194/hess-18-5001-2014.

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Abstract. In the sub-Saharan Sahel, energy and water cycling at the land surface is pivotal for the regional climate, water resources and land productivity, yet it is still very poorly documented. As a step towards a comprehensive climatological description of surface fluxes in this area, this study provides estimates of long-term average annual budgets and seasonal cycles for two main land use types of the cultivated Sahelian belt: rainfed millet crop and fallow bush. These estimates build on the combination of a 7-year field data set from two typical plots in southwestern Niger with detailed

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Wu, Tianjun, Jiancheng Luo, Lijing Gao, et al. "Geo-Object-Based Vegetation Mapping via Machine Learning Methods with an Intelligent Sample Collection Scheme: A Case Study of Taibai Mountain, China." Remote Sensing 13, no. 2 (2021): 249. http://dx.doi.org/10.3390/rs13020249.

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Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and const

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Ervens, B., B. J. Turpin, and R. J. Weber. "Secondary organic aerosol formation in cloud droplets and aqueous particles (aqSOA): a review of laboratory, field and model studies." Atmospheric Chemistry and Physics Discussions 11, no. 8 (2011): 22301–83. http://dx.doi.org/10.5194/acpd-11-22301-2011.

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Abstract. Progress has been made over the past decade in predicting secondary organic aerosol (SOA) mass in the atmosphere using vapor pressure-driven partitioning, which implies that SOA compounds are formed in the gas phase and then partition to an organic phase (gasSOA). However, discrepancies in predicting organic aerosol oxidation state, size and product (molecular mass) distribution, relative humidity (RH) dependence, color, and vertical profile suggest that additional SOA sources and aging processes may be important. The formation of SOA in cloud and aerosol water (aqSOA) is not conside

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Ervens, B., B. J. Turpin, and R. J. Weber. "Secondary organic aerosol formation in cloud droplets and aqueous particles (aqSOA): a review of laboratory, field and model studies." Atmospheric Chemistry and Physics 11, no. 21 (2011): 11069–102. http://dx.doi.org/10.5194/acp-11-11069-2011.

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Abstract. Progress has been made over the past decade in predicting secondary organic aerosol (SOA) mass in the atmosphere using vapor pressure-driven partitioning, which implies that SOA compounds are formed in the gas phase and then partition to an organic phase (gasSOA). However, discrepancies in predicting organic aerosol oxidation state, size and product (molecular mass) distribution, relative humidity (RH) dependence, color, and vertical profile suggest that additional SOA sources and aging processes may be important. The formation of SOA in cloud and aerosol water (aqSOA) is not conside

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Butenko, Olga, and Anna Topchiy. "Modeling fires based on the results of correlation analysis." Ukrainian journal of remote sensing 10, no. 3 (2023): 28–33. http://dx.doi.org/10.36023/ujrs.2023.10.3.245.

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In order to monitor and study in more detail the causes and probability of the occurrence and spread of fires in the east of Ukraine in the combat zone, mathematical modeling of the factors influencing the occurrence of fires based on linear regression was performed in this study. The initial assessment of a priori information presented in a discrete form is a time—consuming process. A large dataset with a time interval requires application of ready—made methods and solutions. By applying statistical analysis techniques and historical analogies, it becomes possible to visually and graphically

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Crespo, Jorge, Martin Reich, Fernando Barra, et al. "Occurrence and Distribution of Silver in the World-Class Río Blanco Porphyry Cu-Mo Deposit, Central Chile." Economic Geology 115, no. 8 (2020): 1619–44. http://dx.doi.org/10.5382/econgeo.4778.

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Abstract Porphyry Cu-Mo deposits (PCDs) are the world’s major source of Cu, Mo, and Re and are also a significant source of Au and Ag. Here we focus on the world-class Río Blanco PCD in the Andes of central Chile, where Ag is a by-product of Cu mining. Statistical examination of an extensive multielemental inductively coupled plasma-mass spectrometry data set indicates compositional trends at the deposit scale, including Ag-Cu (r = 0.71) and Ag-In (r = 0.53) positive correlations, which relate to Cu-Fe sulfides and Cu sulfosalts in the deposit. Silver is primarily concentrated in Cu ores in th

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Ganjdanesh, Alireza, Shangqian Gao, Hirad Alipanah, and Heng Huang. "Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12118–26. http://dx.doi.org/10.1609/aaai.v38i11.29100.

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Generative Adversarial Networks (GANs) have shown remarkable success in modeling complex data distributions for image-to-image translation. Still, their high computational demands prohibit their deployment in practical scenarios like edge devices. Existing GAN compression methods mainly rely on knowledge distillation or convolutional classifiers' pruning techniques. Thus, they neglect the critical characteristic of GANs: their local density structure over their learned manifold. Accordingly, we approach GAN compression from a new perspective by explicitly encouraging the pruned model to preser

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Siqueira, Ricardo Almeida de, Daniel Alejandro Vila, and João Maria de Sousa Afonso. "The Performance of the Diurnal Cycle of Precipitation from Blended Satellite Techniques over Brazil." Remote Sensing 13, no. 4 (2021): 734. http://dx.doi.org/10.3390/rs13040734.

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The knowledge of the diurnal cycle of precipitation is of extreme relevance to understanding the physical/dynamic processes associated with the spatial and temporal distribution of precipitation. The main difficulty of this task is the lack of surface precipitation information over certain regions on an hourly time scale and the low spatial representativeness of these data (normally surface gauges). In order to overcome these difficulties, the main objective of this study is to create a 3-h precipitation accumulation database from the gauge-adjusted daily regional precipitation products to res

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Lopez, Gerardo, Romeo R. Favreau, Colin Smith, and Theodore M. DeJong. "L-PEACH: A Computer-based Model to Understand How Peach Trees Grow." HortTechnology 20, no. 6 (2010): 983–90. http://dx.doi.org/10.21273/hortsci.20.6.983.

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L-PEACH is a computer-based model that simulates the growth of peach [Prunus persica (L.) Batsch] trees. The model integrates important concepts related to carbon assimilation, distribution, and use in peach trees. It also includes modeling of the responses to horticultural practices such as tree pruning and fruit thinning. While running L-PEACH, three-dimensional (3D) depictions of simulated growing trees can be displayed on the computer screen and the user can easily interact with the model. Quantitative data generated during a simulation can be saved to a file or printed for visualization a

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Lopez, Gerardo, Romeo R. Favreau, Colin Smith, and Theodore M. DeJong. "L-PEACH: A Computer-based Model to Understand How Peach Trees Grow." HortTechnology 20, no. 6 (2010): 983–90. http://dx.doi.org/10.21273/horttech.20.6.983.

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L-PEACH is a computer-based model that simulates the growth of peach [Prunus persica (L.) Batsch] trees. The model integrates important concepts related to carbon assimilation, distribution, and use in peach trees. It also includes modeling of the responses to horticultural practices such as tree pruning and fruit thinning. While running L-PEACH, three-dimensional (3D) depictions of simulated growing trees can be displayed on the computer screen and the user can easily interact with the model. Quantitative data generated during a simulation can be saved to a file or printed for visualization a

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Grose, Christopher J., and Juan C. Afonso. "Chemical Disequilibria, Lithospheric Thickness, and the Source of Ocean Island Basalts." Journal of Petrology 60, no. 4 (2019): 755–90. http://dx.doi.org/10.1093/petrology/egz012.

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Abstract We examine REE (Rare-Earth Element) and isotopic (Sr–Hf–Nd–Pb) signatures in OIB (Ocean Island Basalts) as a function of lithospheric thickness and show that the data can be divided into thin- (&lt;12 Ma) and thick-plate (&gt;12 Ma) sub-sets. Comparison to geophysically constrained thermal plate models indicates that the demarcation age (∼12 Ma) corresponds to a lithospheric thickness of about 50 km. Thick-plate OIB show incompatible element and isotopic enrichments, whereas thin-plate lavas show MORB-like or slightly enriched values. We argue that enriched signatures in thick

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Crooks, P., and R. H. Perrott. "Language Constructs for Data Partitioning and Distribution." Scientific Programming 4, no. 2 (1995): 59–85. http://dx.doi.org/10.1155/1995/656010.

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This article presents a survey of language features for distributed memory multiprocessor systems (DMMs), in particular, systems that provide features for data partitioning and distribution. In these systems the programmer is freed from consideration of the low-level details of the target architecture in that there is no need to program explicit processes or specify interprocess communication. Programs are written according to the shared memory programming paradigm but the programmer is required to specify, by means of directives, additional syntax or interactive methods, how the data of the p

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Bo, Shukui, and Yongju Jing. "Data Distribution Partitioning for One-Class Extraction from Remote Sensing Imagery." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 09 (2017): 1754018. http://dx.doi.org/10.1142/s0218001417540180.

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One-class extraction from remotely sensed imagery is researched with multi-class classifiers in this paper. With two supervised multi-class classifiers, Bayesian classifier and nearest neighbor classifier, we firstly analyzed the effect of the data distribution partitioning on one-class extraction from the remote sensing images. The data distribution partitioning refers to the way that the data set is partitioned before classification. As a parametric method, the Bayesian classifier achieved good classification performance when the data distribution was partitioned appropriately. While as a no

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Kurrant, Douglas, Muhammad Omer, Nasim Abdollahi, Pedram Mojabi, Elise Fear, and Joe LoVetri. "Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning." Journal of Imaging 7, no. 1 (2021): 5. http://dx.doi.org/10.3390/jimaging7010005.

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Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms of its ability to reconstruct the properties of various tissue types and identify anomalies. Microwave tomography is an imaging modality that is model-based and reconstructs an approximation of the actual internal spatial distribution of the dielectric properties of a breas

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Kurrant, Douglas, Muhammad Omer, Nasim Abdollahi, Pedram Mojabi, Elise Fear, and Joe LoVetri. "Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning." Journal of Imaging 7, no. 1 (2021): 5. http://dx.doi.org/10.3390/jimaging7010005.

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Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms of its ability to reconstruct the properties of various tissue types and identify anomalies. Microwave tomography is an imaging modality that is model-based and reconstructs an approximation of the actual internal spatial distribution of the dielectric properties of a breas

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AGRAWAL, GAGAN. "DATA DISTRIBUTION ANALYSIS FOR IRREGULAR AND ADAPTIVE CODES." Parallel Processing Letters 09, no. 01 (1999): 135–46. http://dx.doi.org/10.1142/s0129626499000153.

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An important component in compiling for distributed memory machines is data partitioning. While a number of automatic analysis techniques have been proposed for this phase, none of them is applicable for irregular problems. In this paper, we present compile-time analysis for determining data partitioning for such applications. We have developed a set of cost functions for determining communication and redistribution costs in irregular codes. We first determine the appropriate distributions for a single data parallel statement, and then use the cost functions with a greedy algorithm for computi

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Marmor, Yariv N., and Emil Bashkansky. "Reliability of Partitioning Metric Space Data." Mathematics 12, no. 4 (2024): 603. http://dx.doi.org/10.3390/math12040603.

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The process of sorting or categorizing objects or information about these objects into clusters according to certain criteria is a fundamental procedure in data analysis. Where it is feasible to determine the distance metric for any pair of objects, the significance and reliability of the separation can be evaluated by calculating the separation/segregation power (SP) index proposed herein. The latter index is the ratio of the average inter distance to the average intra distance, independent of the scale parameter. Here, the calculated SP value is compared to its statistical distribution obtai

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Kholiev, Vladyslav, and Olesia Barkovska. "Analysis of the of training and test data distribution for audio series classification." Інформаційно-керуючі системи на залізничному транспорті 28, no. 1 (2023): 38–43. http://dx.doi.org/10.18664/ikszt.v28i1.276343.

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The effectiveness of machine learning algorithms for any given task largely depends on the training and test datasets. This manifests itself not only in the amount of data, but also in its content (that is, its relevance for the task at hand), as well as in its organization. Generally, the common approach is to split the dataset into training and testing sets to avoid model overfitting. In addition, to achieve better metrics for the selected criteria (accuracy, learning rate, etc.) of model performance, different ratios of training and test sets are used in the partitioning. The goal of this p

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COENEN, FRANS, and PAUL LENG. "Partitioning strategies for distributed association rule mining." Knowledge Engineering Review 21, no. 1 (2006): 25–47. http://dx.doi.org/10.1017/s0269888906000786.

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In this paper a number of alternative strategies for distributed/parallel association rule mining are investigated. The methods examined make use of a data structure, the T-tree, introduced previously by the authors as a structure for organizing sets of attributes for which support is being counted. We consider six different approaches, representing different ways of parallelizing the basic Apriori-T algorithm that we use. The methods focus on different mechanisms for partitioning the data between processes, and for reducing the message-passing overhead. Both ‘horizontal’ (data distribution) a

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Nguyen Thai, B., and A. Olasz. "RASTER DATA PARTITIONING FOR SUPPORTING DISTRIBUTED GIS PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 543–51. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-543-2015.

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In the geospatial sector big data concept also has already impact. Several studies facing originally computer science techniques applied in GIS processing of huge amount of geospatial data. In other research studies geospatial data is considered as it were always been big data (Lee and Kang, 2015). Nevertheless, we can prove data acquisition methods have been improved substantially not only the amount, but the resolution of raw data in spectral, spatial and temporal aspects as well. A significant portion of big data is geospatial data, and the size of such data is growing rapidly at least by 2

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Erdeljan, A., D. Capko, S. Vukmirovic, D. Bojanic, and V. Congradac. "Distributed PSO Algorithm for Data Model Partitioning in Power Distribution Systems." Journal of Applied Research and Technology 12, no. 5 (2014): 947–57. http://dx.doi.org/10.1016/s1665-6423(14)70601-7.

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Yaşar, Abdurrahman, Muhammed Fati̇h Balin, Xiaojing An, Kaan Sancak, and Ümit V. Çatalyürek. "On Symmetric Rectilinear Partitioning." ACM Journal of Experimental Algorithmics 27 (December 31, 2022): 1–26. http://dx.doi.org/10.1145/3523750.

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Even distribution of irregular workload to processing units is crucial for efficient parallelization in many applications. In this work, we are concerned with a spatial partitioning called rectilinear partitioning (also known as generalized block distribution). More specifically, we address the problem of symmetric rectilinear partitioning of two dimensional domains, and utilize sparse matrices to model them. By symmetric, we mean both dimensions (i.e., the rows and columns of the matrix) are identically partitioned yielding a tiling where the diagonal tiles (blocks) will be squares. We first

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Adli, AWS, FI Prastyasari, DW Handani, and KB Artana. "LNG Distribution Optimization using Set Partitioning Problem Method." IOP Conference Series: Earth and Environmental Science 972, no. 1 (2022): 012082. http://dx.doi.org/10.1088/1755-1315/972/1/012082.

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Abstract The Indonesian government’s commitment to increase the use of gas for domestic demand, by issuing the Decree of the Minister of Energy and Mineral Resources Number 13K/13/MEM/2020 concerning the Assignment of the Implementation of the Supply and Development of LNG Infrastructure and the Gasification 52 power plants in Indonesia. Therefore, a study on the supply chain design that can support the gasification process of the 52 power plants is crucial. Power plant data is imperative to identify receiving terminals which are then grouped into 8 clusters using the K-Means method. The desig

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Jen, Tien-Chien, and Aloysius U. Anagonye. "An Improved Transient Model of Tool Temperatures in Metal Cutting." Journal of Manufacturing Science and Engineering 123, no. 1 (2000): 30–37. http://dx.doi.org/10.1115/1.1334865.

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A model for predicting cutting tool temperatures under transient conditions is presented. The model of Stephenson et al. [10] is extended to include the initial transient response to the tool temperature and nonuniform heat flux distributions. The main goal in this paper is to be able to accurately predict the initial transient tool temperature response, or temperatures in interrupted cutting for cases where the cutting time is short. A method to predict the true transient energy partitioning instead of quasi-steady energy partitioning (Stephenson et al., [10]), without seeking the full numeri

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