Relevant bibliographies by topics / Kaggle

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Kaggle'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Kaggle"

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Сикулер, Денис Валерьевич. "RESOURCES PROVIDING DATA FOR MACHINE LEARNING AND TESTING ARTIFICIAL INTELLIGENCE TECHNOLOGIES." Информационные и математические технологии в науке и управлении, no. 2(22) (June 25, 2021): 39–52. http://dx.doi.org/10.38028/esi.2021.22.2.004.

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В статье выполнен обзор 10 ресурсов сети Интернет, позволяющих подобрать данные для разнообразных задач, связанных с машинным обучением и искусственным интеллектом. Рассмотрены как широко известные сайты (например, Kaggle, Registry of Open Data on AWS), так и менее популярные или узкоспециализированные ресурсы (к примеру, The Big Bad NLP Database, Common Crawl). Все ресурсы предоставляют бесплатный доступ к данным, в большинстве случаев для этого даже не требуется регистрация. Для каждого ресурса указаны характеристики и особенности, касающиеся поиска и получения наборов данных. В работе представлены следующие сайты: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Портал открытых данных Российской Федерации, World Bank, The Big Bad NLP Database, Common Crawl. The work presents review of 10 Internet resources that can be used to find data for different tasks related to machine learning and artificial intelligence. There were examined some popular sites (like Kaggle, Registry of Open Data on AWS) and some less known and specific ones (like The Big Bad NLP Database, Common Crawl). All included resources provide free access to data. Moreover in most cases registration is not needed for data access. Main features are specified for every examined resource, including regarding data search and access. The following sites are included in the review: Kaggle, Google Research, Microsoft Research Open Data, Registry of Open Data on AWS, Harvard Dataverse Repository, Zenodo, Open Data portal of the Russian Federation, World Bank, The Big Bad NLP Database, Common Crawl.
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Al-Taie, Mohammed Zuhair, Naomie Salim, and Adekunle Isiaka Obasa. "Successful Data Science Projects: Lessons Learned from Kaggle Competition." Kurdistan Journal of Applied Research 2, no. 3 (August 27, 2017): 40–49. http://dx.doi.org/10.24017/science.2017.3.18.

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The workflow from data understanding to deployment of an analytical model of a data science project begins at framing the problem at hand, a task that is typically business-oriented and requires human-to-human interaction. However, the next three steps: data understanding, feature extraction, and model building that come next in the pipeline are the key to successful data science projects. Failing to fully understand the requirements of each of these three steps can negatively affect the performance of the proposed system. Hence, the current study tries to answer the following question “What are the requirements of a successful data science project?” To answer this question, we will use the solution that we built to measure the relevance of local search results of small online e-businesses and submitted to Kaggle data science platform to shed light on why our solution did not achieve a top position among other competitors. Evaluation of the design that we submitted to the competition is going to be carried out in the spirit of the three winning submissions. Our results revealed that well-performed data preprocessing, well-defined features, and model ensembling are critical for building successful data science projects. Such a clarification provides insight into specific aspects of model design to help others including Kagglers avoid possible mistakes while approaching their data science projects.
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Lee, Chaehyeon, Jaehyeop Choi, and Heechul Jung. "Deep Learning-based Bengali Handwritten Grapheme Classification for Kaggle Bengali.AI Challenge." Journal of the Institute of Electronics and Information Engineers 57, no. 9 (September 30, 2020): 67–76. http://dx.doi.org/10.5573/ieie.2020.57.9.67.

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Jinfeng, Gao, Sehrish Qummar, Zhang Junming, Yao Ruxian, and Fiaz Gul Khan. "Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection." Computational Intelligence and Neuroscience 2020 (December 15, 2020): 1–11. http://dx.doi.org/10.1155/2020/8864698.

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Diabetic retinopathy (DR) is an eye disease that damages the blood vessels of the eye. DR causes blurred vision or it may lead to blindness if it is not detected in early stages. DR has five stages, i.e., 0 normal, 1 mild, 2 moderate, 3 severe, and 4 PDR. Conventionally, many hand-on projects of computer vision have been applied to detect DR but cannot code the intricate underlying features. Therefore, they result in poor classification of DR stages, particularly for early stages. In this research, two deep CNN models were proposed with an ensemble technique to detect all the stages of DR by using balanced and imbalanced datasets. The models were trained with Kaggle dataset on a high-end Graphical Processing data. Balanced dataset was used to train both models, and we test these models with balanced and imbalanced datasets. The result shows that the proposed models detect all the stages of DR unlike the current methods and perform better compared to state-of-the-art methods on the same Kaggle dataset.
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Assegie, Tsehay Admassu, R. Lakshmi Tulasi, and N. Komal Kumar. "Breast cancer prediction model with decision tree and adaptive boosting." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 184. http://dx.doi.org/10.11591/ijai.v10.i1.pp184-190.

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In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.
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Bratholm, Lars A., Will Gerrard, Brandon Anderson, Shaojie Bai, Sunghwan Choi, Lam Dang, Pavel Hanchar, et al. "A community-powered search of machine learning strategy space to find NMR property prediction models." PLOS ONE 16, no. 7 (July 20, 2021): e0253612. http://dx.doi.org/10.1371/journal.pone.0253612.

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The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.
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Han, Gyeong Jin, and Keuntae Cho. "PLS Path Modeling to Investigate the Relations between Competencies of Data Scientist and Big Data Analysis Performance : Focused on Kaggle Platform." Journal of Korean Institute of Industrial Engineers 42, no. 2 (April 15, 2016): 112–21. http://dx.doi.org/10.7232/jkiie.2016.42.2.112.

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Kiehn, Moritz, Sabrina Amrouche, Paolo Calafiura, Victor Estrade, Steven Farrell, Cécile Germain, Vava Gligorov, et al. "The TrackML high-energy physics tracking challenge on Kaggle." EPJ Web of Conferences 214 (2019): 06037. http://dx.doi.org/10.1051/epjconf/201921406037.

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The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists building on the experience of the successful Higgs Machine Learning challenge in 2014. A training dataset based on a simulation of a generic HL-LHC experiment tracker has been created, listing for each event the measured 3D points, and the list of 3D points associated to a true track.The participants to the challenge should find the tracks in the test dataset, which means building the list of 3D points belonging to each track.The emphasis is to expose innovative approaches, rather than hyper-optimising known approaches. A metric reflecting the accuracy of a model at finding the proper associations that matter most to physics analysis will allow to select good candidates to augment or replace existing algorithms.
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Carpita, Maurizio, Enrico Ciavolino, and Paola Pasca. "Exploring and modelling team performances of the Kaggle European Soccer database." Statistical Modelling 19, no. 1 (January 10, 2019): 74–101. http://dx.doi.org/10.1177/1471082x18810971.

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This study explores a big and open database of soccer leagues in 10 European countries. Data related to players, teams and matches covering seven seasons (from 2009/2010 to 2015/2016) were retrieved from Kaggle, an online platform in which big data are available for predictive modelling and analytics competition among data scientists. Based on both preliminary data analysis, experts’ evaluation and players’ position on the football pitch, role-based indicators of teams’ performance have been built and used to estimate the win probability of the home team with the binomial logistic regression (BLR) model that has been extended including the ELO rating predictor and two random effects due to the hierarchical structure of the dataset. The predictive power of the BLR model and its extensions has been compared with the one of other statistical modelling approaches (Random Forest, Neural Network, k-NN, Naïve Bayes). Results showed that role-based indicators substantially improved the performance of all the models used in both this work and in previous works available on Kaggle. The base BLR model increased prediction accuracy by 10 percentage points, and showed the importance of defence performances, especially in the last seasons. Inclusion of both ELO rating predictor and the random effects did not substantially improve prediction, as the simpler BLR model performed equally good. With respect to the other models, only Naïve Bayes showed more balanced results in predicting both win and no-win of the home team.
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Ben Taieb, Souhaib, and Rob J. Hyndman. "A gradient boosting approach to the Kaggle load forecasting competition." International Journal of Forecasting 30, no. 2 (April 2014): 382–94. http://dx.doi.org/10.1016/j.ijforecast.2013.07.005.

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Dissertations / Theses on the topic "Kaggle"

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Whitley, Michael Aaron. "Using statistical learning to predict survival of passengers on the RMS Titanic." Kansas State University, 2015. http://hdl.handle.net/2097/20541.

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Master of Science
Statistics
Christopher Vahl
When exploring data, predictive analytics techniques have proven to be effective. In this report, the efficiency of several predictive analytics methods are explored. During the time of this study, Kaggle.com, a data science competition website, had the predictive modeling competition, "Titanic: Machine Learning from Disaster" available. This competition posed a classification problem to build a predictive model to predict the survival of passengers on the RMS Titanic. The focus of our approach was on applying a traditional classification and regression tree algorithm. The algorithm is greedy and can over fit the training data, which consequently can yield non-optimal prediction accuracy. In efforts to correct such issues with using the classification and regression tree algorithm, we have implemented cost complexity pruning and ensemble methods such as bagging and random forests. However, no improvement was observed here which may be an artifact associated with the Titanic data and may not be representative of those methods’ performances. The decision trees and prediction accuracy of each method are presented and compared. Results indicate that the predictors sex/title, fare price, age, and passenger class are the most important variables in predicting survival of the passengers.
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Kuvik, Michal. "Rozpoznávání druhu jídla s pomocí hlubokých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400878.

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The aim of this thesis is to study problems of deep convolutional neural networks and the connected classification of images and to experiment with the architecture of particular network with the aim to get the most accurate results on the selected dataset. The thesis is divided into two parts, the first part theoretically outlines the properties and structure of neural networks and briefly introduces selected networks. The second part deals with experiments with this network, such as the impact of data augmentation, batch size and the impact of dropout layers on the accuracy of the network. Subsequently, all results are compared and discussed with the best result achieved an accuracy of 86, 44% on test data.
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Giunchi, Massimiliano. "Reti neurali per la segmentazione di immagini mediche." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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A partire dagli anni '70, con la diffusione dei primi dispositivi per la tomografia assiale computerizzata (TAC), si è assistito ad un'evoluzione sempre più rapida delle modalità con cui si acquisiscono, si conservano e si analizzano le immagini per la diagnosi medica. L’elemento che ha segnato il maggior cambiamento è stato il passaggio da informazioni analogiche, statiche e prettamente bidimensionali come quelle che derivano dalle immagini radiografiche (raggi X), ad informazioni digitali, eventualmente dinamiche e contenenti informazioni tridimensionali. Tutto ciò è stato reso possibile attraverso la transizione da sistemi basati su pellicola a sistemi basati su calcolatori. L’immediata conseguenza di questo sviluppo è stata la necessità di generare algoritmi e metodologie di interpretazione dei dati acquisiti, che simulino al meglio la conoscenza dell’esperto umano, che resta, a tutt'oggi, il più efficiente sistema di elaborazione dei dati medicali conosciuti. Automatizzare il processo di diagnostica, utilizzando un moderno sistema di visione artificiale in grado di segnalare, a partire ad esempio da una radiografia o una risonanza magnetica, quali siano le zone affette da patologia, permetterebbe di velocizzare e rendere più affidabile la diagnosi oltre ad offrire un notevole ausilio a colui che deve redigere un referto medico. Da questo punto di vista gli algoritmi di segmentazione semantica sono in grado di associare un’etichetta o una categoria a ogni pixel dell’immagine e quindi ben si prestano ad identificare in un’immagine medica quali siano le zone che necessitano di un’ulteriore approfondimento. Ma non solo, oggi tali tecniche hanno raggiunto performance tali da essere considerate confrontabili con quelle degli esperti. In questo lavoro di tesi si vogliono identificare e valutare alcune architetture e tecniche in ambito di semantic segmentation per l’individuazione di patologie a partire da immagini radiografiche.
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Nasr, Kamil. "Comparison of Popular Data Processing Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293494.

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Data processing is generally defined as the collection and transformation of data to extract meaningful information. Data processing involves a multitude of processes such as validation, sorting summarization, aggregation to name a few. Many analytics engines exit today for largescale data processing, namely Apache Spark, Apache Flink and Apache Beam. Each one of these engines have their own advantages and drawbacks. In this thesis report, we used all three of these engines to process data from the Carbon Monoxide Daily Summary Dataset to determine the emission levels per area and unit of time. Then, we compared the performance of these 3 engines using different metrics. The results showed that Apache Beam, while offered greater convenience when writing programs, was slower than Apache Flink and Apache Spark. Spark Runner in Beam was the fastest runner and Apache Spark was the fastest data processing framework overall.
Databehandling definieras generellt som insamling och omvandling av data för att extrahera meningsfull information. Databehandling involverar en mängd processer som validering, sorteringssammanfattning, aggregering för att nämna några. Många analysmotorer lämnar idag för storskalig databehandling, nämligen Apache Spark, Apache Flink och Apache Beam. Var och en av dessa motorer har sina egna fördelar och nackdelar. I den här avhandlingsrapporten använde vi alla dessa tre motorer för att bearbeta data från kolmonoxidens dagliga sammanfattningsdataset för att bestämma utsläppsnivåerna per område och tidsenhet. Sedan jämförde vi prestandan hos dessa 3 motorer med olika mått. Resultaten visade att Apache Beam, även om det erbjuds större bekvämlighet när man skriver program, var långsammare än Apache Flink och Apache Spark. Spark Runner in Beam var den snabbaste löparen och Apache Spark var den snabbaste databehandlingsramen totalt.
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Qi, Yi. "Artificial kagome spin ice." College Park, Md. : University of Maryland, 2008. http://hdl.handle.net/1903/8359.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Dept. of Materials Science and Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Leung, Anthony Chi Hin. "Actuation properties of kagome lattice structures." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613328.

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Tan, Zhiming Darren. "Frustrated magnetism in the extended kagome lattice." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:151fb421-198b-44b5-9f0d-8b35333f6450.

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The extended kagome lattice, composed of alternating kagome and triangular layers, provides a novel geometry for frustrated magnetism. In this thesis, we study the properties of Heisenberg spins with nearest-neighbour antiferromagnetic interactions on this lattice. In common with many other models of frustrated magnets, this system has highly degenerate classical ground states. It is set apart from other examples, however, by the strong interlayer correlations between triangular layer spins. We study the implications of such correlations in both the statics and dynamics. We characterise classical ground states using a flux picture for a single layer of kagome spins, a theoretical description that sets geometrical bounds on correlations. We quantify the divergent but sub-extensive ground state degeneracy by a Maxwellian counting argument, and verify this calculation by analysing the energy eigenvalues of numerical ground states. We explore the ground state connectedness but do not reach firm conclusions on this issue. We use the self-consistent Gaussian approximation (SCGA) to calculate static spin correlations at finite temperature. The results of these calculations agree well with elastic neutron scattering experiments. We derive an expression for the effective interlayer interaction between kagome spins by integrating out the triangular lattice spins. We use linear spinwave theory to compute the spin excitation spectrum numerically. This shows encouraging similarity with inelastic neutron scattering data on a single-crystal YBaCo$_4$O$_7$ sample, for a wide range of wavevector and frequency. This agreement shows that our spin model is a reasonable description of the physics, and suggests that this numerical technique might be useful for other geometrically frustrated magnets. We study the dynamics analytically using the stochastic SCGA recently developed for the pyrochlore lattice. For technical reasons, we apply this technique on a related model, the stacked kagome lattice, rather than on the extended kagome lattice itself. From this we find slow relaxation at low temperature, with a rate ~ T2 compared to the faster ~ T scaling for the pyrochlore. Strikingly, in simulations of the dynamics on the extended kagome lattice by numerical integration of the semiclassical equations of motion, we find two different relaxation rates. Kagome layer spins relax more quickly than the triangular layer spins, having ~ T.
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O'Brien, Aroon. "Charge degrees of freedom on the kagome lattice." Doctoral thesis, Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-71860.

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Within condensed matter physics, systems with strong electronic correlations give rise to fascinating phenomena which characteristically require a physical description beyond a one-electron theory, such as high temperature superconductivity, or Mott metal-insulator transitions. In this thesis, a class of strongly correlated electron systems is considered. These systems exhibit fractionally charged excitations with charge +e/2 or -e/2 in two dimensions (2D) and three dimensions (3D), a consequence of both strong correlations and the geometrical frustration of the interactions on the underlying lattices. Such geometrically frustrated systems are typically characterized by a high density of low-lying excitations, leading to various interesting physical effects. This thesis constitutes a study of a model of spinless fermions on the geometrically frustrated kagome lattice. Focus is given in particular to the regime in which nearest-neighbour repulsions V are large in comparison with hopping t between neighbouring sites, the regime in which excitations with fractional charge occur. In the classical limit t = 0, the geometric frustration results in a macroscopically large ground-state degeneracy. This degeneracy is lifted by quantum fluctuations. A low-energy effective Hamiltonian is derived for the spinless fermion model for the case of 1/3 filling in the regime where |t| << V . In this limit, the effective Hamiltonian is given by ring-exchange of order ~ t^3/V^2, lifting the degeneracy. The effective model is shown to be equivalent to a corresponding hard-core bosonic model due to a gauge invariance which removes the fermionic sign problem. The model is furthermore mapped directly to a Quantum Dimer model on the hexagonal lattice. Through the mapping it is determined that the kagome lattice model exhibits plaquette order in the ground state and also that fractional charges within the model are linearly confined. Subsequently a doped version of the effective model is studied, for the case where exactly one spinless fermion is added or subtracted from the system at 1/3 filling. The sign of the newly introduced hopping term is shown to be removable due to a gauge invariance for the case of hole doping. This gauge invariance is a direct result of the bipartite nature of the hole hopping and is confirmed numerically in spectral density calculations. For further understanding of the low-energy physics, a derivation of the model gauge field theory is presented and discussed in relation to the confining quantum electrodynamic in two dimensions. Exact diagonalization calculations illustrate the nature of the fractional charge confinement in terms of the string tension between a bound pair of defects. The calculations employ topological symmetries that exist for the manifold of ground-state configurations. Dynamical calculations of the spectral densities are considered for the full spinless fermion Hamiltonian and compared in the strongly correlated regime with the doped effective Hamiltonian. Calculations for the effective Hamiltonian are then presented for the strongly correlated regime where |t| << V . In the limit g << |t|, the fractional charges are shown to be effectively free in the context of the finite clusters studied. Prominent features of the spectral densities at the Gamma point for the hole and particle contributions are attributed to approximate eigenfunctions of the spinless fermion Hamiltonian in this limit. This is confirmed through an analytical derivation. The case of g ~ t is then considered, as in this case the confinement of the fractional charges is observable in the spectral densities calculated for finite clusters. The bound states for the effectively confined defect pair are qualitatively estimated through the solution of the time-independent Schroedinger equation for a potential which scales linearly with g. The double-peaked feature of spectral density calculations over a range of g values can thus be interpreted as a signature of the confinement of the fractionally charged defect pair. Furthermore, the metal-insulator transition for the effective Hamiltonian is studied for both t > 0 and t < 0. Exact diagonalization calculations are found to be consistent with the predictions of the effective model. Further calculations confirm that the sign of t is rendered inconsequential due to the gauge invariance for g in the regime |t| << V . The charge-order melting metal-insulator transition is studied through density-matrix renormalization group calculations. The opening of the energy gap is found to differ for the two signs of t, reflecting the difference in the band structure at the Fermi level in each case. The qualitative nature of transition in each case is discussed. As a step towards a realization of the model in experiment, density-density correlation functions are introduced and such a calculation is shown for the plaquette phase for the effective model Hamiltonian at 1/3 filling in the absence of defects. Finally, the open problem of statistics of the fractional charges is discussed.
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Silva, Junior José Luiz Ferreira da. "Efeito Kondo e magnetismo em uma rede Kagome." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/53142.

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Neste trabalho estudamos o modelo da rede de Kondo em uma rede kagome, buscando uma maior compreensão dos efeitos da frustração geométrica em sistemas de férmions pesados. Para tanto, fizemos uma aproximação de campo médio no hamiltoniano do sistema que serve para todas as fases do sistema. Analisamos inicialmente o caso não magnético. Obtemos neste limite as energias eletrônicas e as funções de Green necessárias ao cálculo numérico autoconsistente das ocupações e do parâmetro de Kondo. Os resultados encontrados estão em concordância qualitativa com trabalhos publicados em outras geometrias. A seguir analisamos o caso magnético, onde introduzimos uma aproximação suplementar, a qual é compatível com a de campo médio já considerada e, em princípio, existente apenas em sistemas com frustração geométrica. Realizamos cálculos autoconsistentes através de somas sobre as frequências de Matsubara. Os resultados mostram que não há coexistência entre ordem magnética e efeito Kondo, além de haver a supressão do antiferromagnetismo com o aumento de temperatura e variações no preenchimento de bandas.
In this work we study the Kondo Lattice model for the kagome lattice, in order to understand better the effects of geometrical frustration in heavy-fermion systems. In this context, we consider a mean field scheme valid for all the system’s phases. Firstly, we analyzed the nonmagnetic case. In this approximation the electron energies and spectral functions are reachable, then we use the density of states to calculate the occupations selfconsistently. Our results are qualitatively compared with previous works in other geometries. In the second part we introduce an approximation for magnestism, which takes into account the mean field scheme considered and the presence of geometrical frustration. Self-consistent calculations are done through the frequencies summation method. Our results show that the magnetism is supressed when the temperature is increased or the band filling deviates from half-filling. Besides, the coexistence of magnetic order and Kondo effect is not observable.
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Pettigrew, Keith G. "Magnetic properties of the jarosites : model kagome antiferromagnets." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/15642.

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The long-range ordered ground state of KFe3(SO4)2(OH)6 has been determined unambiguously through Spherical Neutron Polarimetry on a natural single crystal. The magnetic arrangement has been shown to be the q = 0 structure with triangles of only positive chirality. A new transition to a state of higher magnetisation has been identified in a synthetic sample at T ~ 7K. The driving force for this has been attributed to anisotropy in the system and theory provides a possible ordering mechanism with weak ferromagnetic order. Magnetic neutron scattering confirms an intermediate phase at 52K < T < 65K with the coexistence of Bragg peaks and significant diffuse scattering. D3OFe3(SO4)2(OD)6 has been shown to exhibit spin-glass type behaviour with a freezing temperature, Tf, that is dependent on the timescale of the measurement. Intentional dilution of the magnetic lattice with Al3+ induces a transition to long-range order at a temperature dependent on the level of doping. Mössbauer spectroscopy shows two equally-weighed magnetic sites with local field value and probability in excellent agreement with theoretical predictions. A new mixed-cation compound, H3OFe2.4Cr0.3(SO4)2(OH)6, has been successfully prepared and represents the first experimental example of mixed magnetic ions on a kagome lattice. Field-cooled (FC) and zero-field-cooled (ZFC) dc susceptibility data cross at a temperature dependent on the magnitude of the applied field and hysteresis measurements suggest the presence of ferrimagnetism. High resolution neutron diffraction on D3OCr3(SO4)2(OD)6 has shown that the crystal structure is slightly distorted from the R ?m to the R 3 m space group. A magnetic transition is observed at 2K through a sharp peak in ac susceptibility accompanied by a small FC/ZFC splitting. Polarised neutron diffraction shows no change in the magnetic correlation length, ξ, on cooling from 15K to 0.25K where only short-range order exists with ξ ~ 6.6Å, about half the width of a kagome star.
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Books on the topic "Kaggle"

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Shi, Feng. Learn About Observed Frequencies in SPSS With Data From the Kaggle ML and Data Science Survey (2017). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd., 2019. http://dx.doi.org/10.4135/9781526474872.

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Cētankumār, Liṅgadahaḷḷi. Gōlibār gaurmeṇṭalli raitara kaggole. Beṅgaḷūru: Kārtik Eṇṭarpraises, 2007.

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Cētankumār, Liṅgadahaḷḷi. Gōlibār gaurmeṇṭalli raitara kaggole. Beṅgaḷūru: Kārtik Eṇṭarpraises, 2007.

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Siraj, Sayyad Mostafa. Kagaje raker dag. Calcutta: Dey's, 1990.

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Naengdam kagye. Sŏul-si: Hyŏnamsa, 2015.

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Rāmanāth, En. Maṅkudiṇṇeya kagga. Beṅgaḷūru: Aḷilu Sēvā Saṃsthe, 2006.

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Tolnai, Ottó. A tengeri kagyló: Kisregély. Újvidék: Forum Könyvkiadó, 2011.

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Li︠u︡bich, Pavel. "Kagulʺ": Russkie pravoslavnye starovery. Kishinev: [publisher not identified], 2012.

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Seichō, Matsumoto. Rantō: Edo kagee. Tōkyō: Kadokawa Shoten, 1987.

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Atōda, Takashi. Kagee no machi. Tōkyō: Kadokawa Shoten, 1989.

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Book chapters on the topic "Kaggle"

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Fleischmann, Ulrich. "Kagame, Alexis." In Kindlers Literatur Lexikon (KLL), 1. Stuttgart: J.B. Metzler, 2020. http://dx.doi.org/10.1007/978-3-476-05728-0_4068-1.

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Awaga, Kunio, Nobuo Wada, Isao Watanabe, and Tamotsu Inabe. "Organic Kagome Antiferromagnets." In Magnetism: Molecules to Materials, 121–09. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2003. http://dx.doi.org/10.1002/9783527620548.ch3a.

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Gottwald, Clytus. "Kagel, Mauricio." In Komponisten Lexikon, 306–10. Stuttgart: J.B. Metzler, 2003. http://dx.doi.org/10.1007/978-3-476-05274-2_156.

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Reich, Wieland. "Mauricio Kagel." In Kammermusikführer, 330–33. Stuttgart: J.B. Metzler, 1998. http://dx.doi.org/10.1007/978-3-476-03514-1_66.

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Gottwald, Clytus. "Kagel, Mauricio." In Metzler Komponisten Lexikon, 392–97. Stuttgart: J.B. Metzler, 1992. http://dx.doi.org/10.1007/978-3-476-03421-2_157.

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Gottwald, Clytus. "Zu Kagels akustischer Theologie." In Neue Musik als spekulative Theologie, 69–83. Stuttgart: J.B. Metzler, 2003. http://dx.doi.org/10.1007/978-3-476-02923-2_4.

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Podur, Justin. "The State Kagame Built." In America's Wars on Democracy in Rwanda and the DR Congo, 257–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44699-4_12.

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Ishizuka, Hiroaki. "Anomalous Hall Insulator in Kagome Ice." In Magnetism and Transport Phenomena in Spin-Charge Coupled Systems on Frustrated Lattices, 79–91. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55663-3_6.

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Masolo, D. A. "Alexis Kagame and African socio-linguistics." In African Philosophy, 181–205. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3517-4_8.

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Ishizuka, Hiroaki. "Thermally-Induced Phases on a Kagome Lattice." In Magnetism and Transport Phenomena in Spin-Charge Coupled Systems on Frustrated Lattices, 65–77. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55663-3_5.

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Conference papers on the topic "Kaggle"

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Eren, Maksim Ekin, Nick Solovyev, Edward Raff, Charles Nicholas, and Ben Johnson. "COVID-19 Kaggle Literature Organization." In DocEng '20: ACM Symposium on Document Engineering 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3395027.3419591.

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Eren, Maksim E., Nick Solovyev, Chris Hamer, Renee McDonald, Boian S. Alexandrov, and Charles Nicholas. "COVID-19 multidimensional kaggle literature organization." In DocEng '21: ACM Symposium on Document Engineering 2021. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3469096.3474927.

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Kainkaryam, S., C. Ong, S. Sen, and A. Sharma. "Crowdsourcing Salt Model Building: Kaggle-TGS Salt Identification Challenge." In 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201901271.

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CHIMIDT, RAFAEL FABRI, and ERNEE KOZYREFF FILHO. "Análise exploratória de uma década de competições no Kaggle." In ENEGEP 2020 - Encontro Nacional de Engenharia de Produção. ENEGEP 2020 - Encontro Nacional de Engenharia de Produção, 2020. http://dx.doi.org/10.14488/enegep2020_tn_sto_344_1771_41218.

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Quaranta, Luigi, Fabio Calefato, and Filippo Lanubile. "KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle." In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR). IEEE, 2021. http://dx.doi.org/10.1109/msr52588.2021.00072.

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Farag, Nadine, and Ghada Hassan. "Predicting the Survivors of the Titanic Kaggle, Machine Learning From Disaster." In the 7th International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3220267.3220282.

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Tauchert, Christoph, Peter Buxmann, and Jannis Lambinus. "Crowdsourcing Data Science: A Qualitative Analysis of Organizations’ Usage of Kaggle Competitions." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2020. http://dx.doi.org/10.24251/hicss.2020.029.

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Mangal, Ankita, and Nishant Kumar. "Using big data to enhance the bosch production line performance: A Kaggle challenge." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840826.

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Narayanan, Arvind, Elaine Shi, and Benjamin I. P. Rubinstein. "Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge." In 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose). IEEE, 2011. http://dx.doi.org/10.1109/ijcnn.2011.6033446.

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A. Noever, David, and Samantha E. Miller Noever. "Image Classifiers for Network Intrusions." In 9th International Conference of Security, Privacy and Trust Management (SPTM 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110504.

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Abstract:
This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2’s convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important sourcedestination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle.
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Reports on the topic "Kaggle"

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Saxena, Avadh. PT-Symmetric Kagome Lattices and Localization. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1363733.

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