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Academic literature on the topic 'Aggregated data'

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Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Aggregated data"

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HASSANI, HOSSEIN, ABDOL SOOFI, and MOHAMMAD SADEGH AVAZALIPOUR. "FORECASTING GDP WITH AGGREGATED AND SECTORAL DATA." Fluctuation and Noise Letters 10, no. 03 (September 2011): 249–65. http://dx.doi.org/10.1142/s0219477511000533.

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We use the Singular Spectrum Analysis (SSA), a forecasting method which is based on the noise reduction procedure, in prediction of the Iranian gross domestic product (GDP). Two different approaches are considered in forecasting the series. In the first approach, we apply SSA to the aggregate GDP series. In the second approach, we predict the GDP by first forecasting the GDP of the sectors of the economy, and then sum the predicted values as the forecast of the aggregate GDP. We measured the prediction accuracy of both approaches using various criteria, and found that predictions based on the disaggregated, sectoral GDP tend to outperform the predictions based on the aggregated data.
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Jarjoura, D. "Inferences from Aggregated Data." Academic Emergency Medicine 10, no. 8 (August 1, 2003): 881–82. http://dx.doi.org/10.1197/aemj.10.8.881.

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Aadland, David. "Detrending time-aggregated data." Economics Letters 89, no. 3 (December 2005): 287–93. http://dx.doi.org/10.1016/j.econlet.2005.06.001.

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Toth, Daniell. "Data Smearing: An Approach to Disclosure Limitation for Tabular Data." Journal of Official Statistics 30, no. 4 (December 1, 2014): 839–57. http://dx.doi.org/10.2478/jos-2014-0050.

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Abstract Statistical agencies often collect sensitive data for release to the public at aggregated levels in the form of tables. To protect confidential data, some cells are suppressed in the publicly released data. One problem with this method is that many cells of interest must be suppressed in order to protect a much smaller number of sensitive cells. Another problem is that the covariates used to aggregate and level of aggregation must be fixed before the data is released. Both of these restrictions can severely limit the utility of the data. We propose a new disclosure limitation method that replaces the full set of microdata with synthetic data for use in producing released data in tabular form. This synthetic data set is obtained by replacing each unit’s values with a weighted average of sampled values from the surrounding area. The synthetic data is produced in a way to give asymptotically unbiased estimates for aggregate cells as the number of units in the cell increases. The method is applied to the U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages data, which is released to the public quarterly in tabular form and aggregated across varying scales of time, area, and economic sector.
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Seater, John J. "TESTING THE PERMANENT-INCOME/LIFE-CYCLE HYPOTHESIS WITH AGGREGATE DATA." Macroeconomic Dynamics 2, no. 3 (September 1998): 401–25. http://dx.doi.org/10.1017/s1365100598008062.

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The aggregate implications of the permanent-income/life-cycle hypothesis (PILCH) are derived rigorously. Virtually all empirical rejections of PILCH based on aggregated data are shown to result from misspecifications or from characteristics of aggregate data that have been overlooked. Valid aggregate tests are proposed. Those based on a properly formulated aggregate consumption function may be superior to those based on Euler-equation methods.
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Gelsema, Tjalling. "The Logic of Aggregated Data." Acta Cybernetica 24, no. 2 (November 3, 2019): 211–48. http://dx.doi.org/10.14232/actacyb.24.2.2019.4.

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A notion of generalization-specialization is introduced that is more expressive than the usual notion from, e.g., the UML or RDF-based languages. This notion is incorporated in a typed formal language for modeling aggregated data. Soundness with respect to a sets-and-functions semantics is shown subsequently. Finally, a notion of congruence is introduced. With it terms in the language that have identical semantics, i.e., synonyms, can be discovered. The resulting formal language is well-suited for capturing faithfully aggregated data in such a way that it can serve as the foundation for corporate metadata management in a statistical office.
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Linton, Oliver, and Yoon-Jae Whang. "NONPARAMETRIC ESTIMATION WITH AGGREGATED DATA." Econometric Theory 18, no. 2 (April 2002): 420–68. http://dx.doi.org/10.1017/s0266466602182089.

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We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intrafamily component but require that observations from different families be independent. We establish consistency and asymptotic normality for our procedures. As usual, the rates of convergence can be very slow depending on the behavior of the characteristic function at infinity. We investigate the practical performance of our method in a simple Monte Carlo experiment.
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Bowman, K. O., and M. A. Kastenbaum. "Overdispersion of aggregated genetic data." Mutation Research/Environmental Mutagenesis and Related Subjects 272, no. 2 (October 1992): 133–37. http://dx.doi.org/10.1016/0165-1161(92)90041-j.

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Nicoletti, Cheti, and Nicky Best. "Quantile regression with aggregated data." Economics Letters 117, no. 2 (November 2012): 401–4. http://dx.doi.org/10.1016/j.econlet.2012.06.011.

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Hu, Jinyu, Juan Luo, Yuxi Zhang, Panwu Wang, and Yu Liu. "Location-Based Data Aggregation in 6LoWPAN." International Journal of Distributed Sensor Networks 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/912926.

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Location-based information has recently been exploited to assist the aggregated process of data, thereby reducing the spatial redundancy efficiently. The constraints nature in 6LoWPAN becomes one of the major concerns in data aggregation methods. However, traditional CSMA/CA in MAC layer may cause significant transmission and control overhead as well as delay on listening and competing for channels. It is a low efficient way to transfer IPv6 packet due to the big packet header. To overcome these shortages, in this paper, we propose LDAA, a location-based novel data aggregation model that aggregates data from the network layer according to the MAC layer queuing delay. When the queuing delay becomes larger, more packets will be dynamically aggregated into one packet to increase the proportion of application data. Otherwise, the amount of packets involved in aggregation will decrease to improve channels utilization. Simulation results show that our approach could provide better real-time guarantees and reduce data spatial redundancy and energy consumption efficiently.
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Dissertations / Theses on the topic "Aggregated data"

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Tanaka, Yusuke. "Probabilistic Models for Spatially Aggregated Data." Kyoto University, 2020. http://hdl.handle.net/2433/253422.

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Mouhoub, Mohamed Lamine. "Aggregated Search of Data and Services." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLED066/document.

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Ces dernières années ont témoigné du succès du projet Linked Open Data (LOD) et de la croissance du nombre de sources de données sémantiques disponibles sur le web. Cependant, il y a encore beaucoup de données qui ne sont pas encore mises à disposition dans le LOD telles que les données sur demande, les données de capteurs etc. Elles sont néanmoins fournies par des API des services Web. L'intégration de ces données au LOD ou dans des applications de mashups apporterait une forte valeur ajoutée. Cependant, chercher de tels services avec les outils de découverte de services existants nécessite une connaissance préalable des répertoires de services ainsi que des ontologies utilisées pour les décrire.Dans cette thèse, nous proposons de nouvelles approches et des cadres logiciels pour la recherche de services web sémantiques avec une perspective d'intégration de données. Premièrement, nous introduisons LIDSEARCH, un cadre applicatif piloté par SPARQL pour chercher des données et des services web sémantiques.De plus, nous proposons une approche pour enrichir les descriptions sémantiques de services web en décrivant les relations ontologiques entre leurs entrées et leurs sorties afin de faciliter l'automatisation de la découverte et de la composition de services. Afin d'atteindre ce but, nous utilisons des techniques de traitement automatique de la langue et d'appariement de textes basées sur le deep-learning pour mieux comprendre les descriptions des services.Nous validons notre travail avec des preuves de concept et utilisons les services et les ontologies d'OWLS-TC pour évaluer nos approches proposées de sélection et d'enrichissement
The last years witnessed the success of the Linked Open Data (LOD) project as well as a significantly growing amount of semantic data sources available on the web. However, there are still a lot of data not being published as fully materialized knowledge bases like as sensor data, dynamic data, data with limited access patterns, etc. Such data is in general available through web APIs or web services. Integrating such data to the LOD or in mashups would have a significant added value. However, discovering such services requires a lot of efforts from developers and a good knowledge of the existing service repositories that the current service discovery systems do not efficiently overcome.In this thesis, we propose novel approaches and frameworks to search for semantic web services from a data integration perspective. Firstly, we introduce LIDSEARCH, a SPARQL-driven framework to search for linked data and semantic web services. Moreover, we propose an approach to enrich semantic service descriptions with Input-Output relations from ontologies to facilitate the automation of service discovery and composition. To achieve such a purpose, we apply natural language processing techniques and deep-learning-based text similarity techniques to leverage I/O relations from text to ontologies.We validate our work with proof-of-concept frameworks and use OWLS-TC as a dataset for conducting our experiments on service search and enrichment
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Samita, Sembakutti. "Analysis of aggregated plant disease incidence data." Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/27331.

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If diseased plants (or plant units) are randomly dispersed, the frequency distribution of diseased plants (or plant units) per sample may be described by a binomial distribution, and statistical analyses may be based on the linear logistic model. Since most disease incidence data do not have a random spatial pattern, the binomial distribution can hardly ever, in practice, be used to describe observed frequencies. In this study, the use of conditional probability distributions, such as the logistic-normal binomial distribution, for such data is illustrated. Both descriptive distribution fitting and statistical modelling are discussed. The study evaluates several methods for analysis of incidence data which do not exhibit a random spatial pattern. Some of these methods are applied to plant disease data for the first time. A method of choosing between the different analyses is discussed. All the techniques are illustrated using examples and, as an application, survey data collected on pineapple wilt disease in Sri Lanka are extensively studied. As an alternative method of describing disease incidence data with a non random spatial pattern, the use of two-dimensional distance class (2DCLASS) analysis was evaluated using the same survey data. 2DCLASS analysis is widely accepted in plant disease epidemiology as a method of analysing non-random spatial patterns when the observations are made as presence or absence of the disease on individual plant basis. We demonstrate the possibility of using quadrat-based data in 2DCLASS analysis. We investigate the use of 2DCLASS analysis as a methodology and find some drawbacks with this technique, which are discussed in detail. Moreover, this study introduces a new parameter in the 2DCLASS analysis called Scaled Core Cluster size, that may be more suitable to use for comparison of datasets of different sizes.
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Rastogi, Tanay. "Load Identification from Aggregated Data using Generative Modeling." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249599.

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In the view of an exponential increase in demand for energy, there is a need to come up with a sustainable energy consumption system in residential buildings. Several pieces of research show that this can be achieved by providing real-time energy consumption feedback of each appliance to its residents. This can be achieved through Non-Intrusive Load Monitoring System (NILM) that disaggregates the electricity consumption of individual appliances from the total energy consumption of a household. The state-of-art NILM have several challenges that preventing its large-scale implementation due to its limited applicability and scalability on different households. Most of the NILM research only trains the inference model for a specific house with a limited set of appliances and does not create models that can generalize appliances that are not present in the dataset. In this Master thesis, a novel approach is proposed to tackle the above-mentioned issue in the NILM. The thesis propose to use a Gaussian Mixture Model (GMM) procedure to create a generalizable electrical signature model for each appliance type by training over labelled data from different appliances of the same type and create various combinations of appliances by merging the generated models. Maximum likelihood estimation method is used to label the unlabeled aggregated data and disaggregate it into individual appliances. As a proof of concept, the proposed algorithm is evaluated on two datasets, Toy dataset and ACSF2 dataset, and is compared with a modified version of state-of-the-art RNN network on ACS-F2 dataset. For evaluation, Precision, Recall and F-score metrics are used on all the implementations. From the evaluation, it can be stated that the GMM procedure can create a generalizable appliance signature model, can disaggregate the aggregated data and label previously unseen appliances. The thesis work also shows that given a small set of training data, the proposed algorithm performs better than RNN implementation. On the other hand, the proposed algorithm highly depends on the quality of the data. The algorithm also fails to create an accurate model for appliances due to the poor initialization of parameters for the GMM. In addition, the proposed algorithm suffers from the same inaccuracies as the state of art.
På grund av den exponentiella ökningen av energi-efterfrågan är det nödvändigt att komma fram till ett hållbart energiförbrukningssystem i bostäder. Flera undersökningar visar att detta kan uppnås genom att upplysa användaren om energikonsumtionen för varje apparat i huset. Detta kan uppnås genom ett icke-störande övervakningssystem som visar belastningen (NILM) och skiljer elförbrukningen hos enskilda apparater från hushållets totala energiförbrukning. Det senaste NILM har flera utmaningar som försvårar ett omfattande genomförande på grund av begränsad lämplighet hos olika hushåll. I forskningen inom NILM tränas oftast endast inferensmodellen för ett specifikt hus med ett begränsat antal apparater och skapar inte modeller som kan generalisera till apparater som inte finns i datasetet. I detta examensarbete föreslås ett nytt tillvägagångssätt för att angripa det ovan nämnda problemet med NILM. Arbetet avser att använda en Gaussian Mixture Model, GMM-teknik, för att skapa en generaliserbar elektrisk signaturmodell för varje typ av apparat genom att träna över markerade data från olika apparater av samma typ och skapa olika kombinationer av apparater genom att slå samman de genererade modellerna. Maximum likelihood-metoden används för att markera omärkta aggregerade data och disaggregera data i enskilda apparater. Som ett bevis på konceptet utvärderas den föreslagna algoritmen på två dataset, Toy-datasetet och ACS-F2- datasetet, och jämförs med en modifierad version av det senaste RNN- nätverket på ACS-F2-datasetet. Precision, Recall och F-score är mätetal som används för utvärdering av alla implementeringar. Från utvärderingen kan det konstateras att GMM-förfarandet kan skapa en generaliserbar signaturmodell, kan disaggregera aggregerade data och markera tidigare osynliga apparater. Examensarbetet visar också att, givet en liten uppsättning av träningsdata, så har den föreslagna algoritmen bättre prestanda än RNNgenomförandet. Å andra sidan är den föreslagna algoritmen väldigt beroende av kvaliteten hos data. Algoritmen misslyckas också med att skapa en exakt modell för apparater på grund av den dåliga initialiseringen av parametrar för GMM. Dessutom lider den föreslagna algoritmen av samma felaktigheter som den aktuella modellen.
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Folia, Maria Myrto. "Inference in stochastic systems with temporally aggregated data." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/inference-in-stochastic-systems-with-temporally-aggregated-data(17940c86-e6b3-4f7d-8a43-884bbf72b39e).html.

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The stochasticity of cellular processes and the small number of molecules in a cell make deterministic models inappropriate for modelling chemical reactions at the single cell level. The Chemical Master Equation (CME) is widely used to describe the evolution of biochemical reactions inside cells stochastically but is computationally expensive. The Linear Noise Approximation (LNA) is a popular method for approximating the CME in order to carry out inference and parameter estimation in stochastic models. Data from stochastic systems is often aggregated over time. One such example is in luminescence bioimaging, where a luciferase reporter gene allows us to quantify the activity of proteins inside a cell. The luminescence intensity emitted from the luciferase experiments is collected from single cells and is integrated over a time period (usually 15 to 30 minutes), which is then collected as a single data point. In this work we consider stochastic systems that we approximate using the Linear Noise Approximation (LNA). We demonstrate our method by learning the parameters of three different models from which aggregated data was simulated, an Ornstein-Uhlenbeck model, a Lotka-Voltera model and a gene transcription model. We have additionally compared our approach to the existing approach and find that our method is outperforming the existing one. Finally, we apply our method in microscopy data from a translation inhibition experiment.
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Davis, Brett Andrew, and Brett Davis@abs gov au. "Inference for Discrete Time Stochastic Processes using Aggregated Survey Data." The Australian National University. Faculty of Economics and Commerce, 2003. http://thesis.anu.edu.au./public/adt-ANU20040806.104137.

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We consider a longitudinal system in which transitions between the states are governed by a discrete time finite state space stochastic process X. Our aim, using aggregated sample survey data of the form typically collected by official statistical agencies, is to undertake model based inference for the underlying process X. We will develop inferential techniques for continuing sample surveys of two distinct types. First, longitudinal surveys in which the same individuals are sampled in each cycle of the survey. Second, cross-sectional surveys which sample the same population in successive cycles but with no attempt to track particular individuals from one cycle to the next. Some of the basic results have appeared in Davis et al (2001) and Davis et al (2002).¶ Longitudinal surveys provide data in the form of transition frequencies between the states of X. In Chapter Two we develop a method for modelling and estimating the one-step transition probabilities in the case where X is a non-homogeneous Markov chain and transition frequencies are observed at unit time intervals. However, due to their expense, longitudinal surveys are typically conducted at widely, and sometimes irregularly, spaced time points. That is, the observable frequencies pertain to multi-step transitions. Continuing to assume the Markov property for X, in Chapter Three, we show that these multi-step transition frequencies can be stochastically interpolated to provide accurate estimates of the one-step transition probabilities of the underlying process. These estimates for a unit time increment can be used to calculate estimates of expected future occupation time, conditional on an individual’s state at initial point of observation, in the different states of X.¶ For reasons of cost, most statistical collections run by official agencies are cross-sectional sample surveys. The data observed from an on-going survey of this type are marginal frequencies in the states of X at a sequence of time points. In Chapter Four we develop a model based technique for estimating the marginal probabilities of X using data of this form. Note that, in contrast to the longitudinal case, the Markov assumption does not simplify inference based on marginal frequencies. The marginal probability estimates enable estimation of future occupation times (in each of the states of X) for an individual of unspecified initial state. However, in the applications of the technique that we discuss (see Sections 4.4 and 4.5) the estimated occupation times will be conditional on both gender and initial age of individuals.¶ The longitudinal data envisaged in Chapter Two is that obtained from the surveillance of the same sample in each cycle of an on-going survey. In practice, to preserve data quality it is necessary to control respondent burden using sample rotation. This is usually achieved using a mechanism known as rotation group sampling. In Chapter Five we consider the particular form of rotation group sampling used by the Australian Bureau of Statistics in their Monthly Labour Force Survey (from which official estimates of labour force participation rates are produced). We show that our approach to estimating the one-step transition probabilities of X from transition frequencies observed at incremental time intervals, developed in Chapter Two, can be modified to deal with data collected under this sample rotation scheme. Furthermore, we show that valid inference is possible even when the Markov property does not hold for the underlying process.
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Marklund, Emil. "Bayesian inference in aggregated hidden Markov models." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243090.

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Single molecule experiments study the kinetics of molecular biological systems. Many such studies generate data that can be described by aggregated hidden Markov models, whereby there is a need of doing inference on such data and models. In this study, model selection in aggregated Hidden Markov models was performed with a criterion of maximum Bayesian evidence. Variational Bayes inference was seen to underestimate the evidence for aggregated model fits. Estimation of the evidence integral by brute force Monte Carlo integration theoretically always converges to the correct value, but it converges in far from tractable time. Nested sampling is a promising method for solving this problem by doing faster Monte Carlo integration, but it was here seen to have difficulties generating uncorrelated samples.
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Broc, Camilo. "Variable selection for data aggregated from different sources with group of variable structure." Thesis, Pau, 2019. http://www.theses.fr/2019PAUU3048.

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Durant les dernières décennies, la quantité de données disponibles en génétique a consi-dérablement augmenté. D’une part, une amélioration des technologies de séquençage demolécules a permis de réduire fortement le coût d’extraction du génome humain. D’autrepart, des consortiums internationaux d’institutions ont permis la mise en commun de lacollecte de données sur de larges populations. Cette quantité de données nous permetd’espérer mieux comprendre les mécanismes régissant le fonctionnement de nos cellules.Dans ce contexte, l’épidémiologie génétique est un domaine cherchant à déterminer larelation entre des caractéristiques génétiques et l’apparition d’une maladie. Des méthodesstatistiques spécifiques à ce domaine ont dû être développées, en particulier à cause desdimensions que les données présentent : en génétique, l’information est contenue dans unnombre de variables grand par rapport au nombre d’observations.Dans cette dissertation, deux contributions sont présentées. Le premier projet appeléPIGE (Pathway-Interaction Gene Environment) développe une méthode pour déterminerdes interactions gène-environnement. Le second projet vise à développer une méthode desélection de variables adaptée à l’analyse de données provenant de différentes études etprésentant une structure de groupe de variables.Le document est divisé en six parties. Le premier chapitre met en relief le contexte,d’un point de vue à la fois biologique et mathématique. Le deuxième chapitre présente lesmotivations de ce travail et la mise en œuvre d’études en épidémiologie génétique. Le troi-sième chapitre aborde les questions relatives à l’analyse d’interactions gène-environnementet la première contribution de la thèse y est présentée. Le quatrième chapitre traite desproblématiques de méta-analyses. Le développement d’une nouvelle méthode de réductionde dimension répondant à ces questions y est présenté. Le cinquième chapitre met en avantla pertinence de la méthode dans des cas de pleiotropie. Enfin, le sixième et dernier chapitredresse un bilan du travail présenté et dresse des perspectives pour le futur
During the last decades, the amount of available genetic data on populations has growndrastically. From one side, a refinement of chemical technologies have made possible theextraction of the human genome of individuals at an accessible cost. From the other side,consortia of institutions and laboratories around the world have permitted the collectionof data on a variety of individuals and population. This amount of data raised hope onour ability to understand the deepest mechanisms involved in the functioning of our cells.Notably, genetic epidemiology is a field that studies the relation between the geneticfeatures and the onset of a disease. Specific statistical methods have been necessary forthose analyses, especially due to the dimensions of available data: in genetics, informationis contained in a high number of variables compared to the number of observations.In this dissertation, two contributions are presented. The first project called PIGE (Pathway-Interaction Gene Environment) deals with gene-environment interaction assessments.The second one aims at developing variable selection methods for data which has groupstructures in both the variables and the observations.The document is divided into six chapters. The first chapter sets the background of this work,where both biological and mathematical notations and concepts are presented and gives ahistory of the motivation behind genetics and genetic epidemiology. The second chapterpresent an overview of the statistical methods currently in use for genetic epidemiology.The third chapter deals with the identification of gene-environment interactions. It includesa presentation of existing approaches for this problem and a contribution of the thesis. Thefourth chapter brings off the problem of meta-analysis. A definition of the problem and anoverview of the existing approaches are presented. Then, a new approach is introduced.The fifth chapter explains the pleiotropy studies and how the method presented in theprevious chapter is suited for this kind of analysis. The last chapter compiles conclusionsand research lines for the future
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Molitor, Torsten. "Coverage Prediction for Inter-Frequency Handover using Machine Learning with Aggregated Training Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286676.

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An important application of Machine Learning (ML) in mobile networks is to predict if a user device has coverage on a frequency other than the current serving frequency. This is a use-case called Secondary Carrier Prediction (SCP). In this thesis we deduce if data across different cells and frequencies can be successfully combined when learning this task, thus reducing the number of models that require training. Aggregation of data involves several challenges, such as different prevalences and varying amounts of available data, but more importantly the possibility of achieving synergies in training by exploiting recurring patterns in data. By using an experimental setup in which models are trained and validated on aggregated datasets it is shown that synergies in fact can be achieved through aggregation. The scalability of this task is improved so that the number of models can be reduced with a factor as large as the number of cells times the number of frequencies, while maintaining similar or improved prediction performance.
Prediktion av täckning på sekundära frekvenser är en signifikant tillämpning av maskininlärning inom mobila nätverk. I den här avhandlingen utreds möjligheten att träna modeller på aggregationer av data, med följden att antalet modeller blir färre. Olika klassbalanser och varierande tillgång på data är utmaningar som uppstår vid aggregation, men även möjligheten att uppnå synergier genom att utnyttja återkommande mönster i datat. Med en experimentell uppställning där modeller tränas och valideras på aggregerade dataset visas att synergier kan uppnås genom aggregation. Skalbarheten på denna tillämpning förbättras till den grad att antalet modeller kan reduceras med en faktor lika stor som antalet celler gånger antalet frekvenser, med likvärdig eller förbättrad prediktionsprestanda.
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MacKelvie, Erin. "A Comparison of Traditional Aggregated Data to a Comprehensive Second-by-Second Data Depiction in Functional Analysis Graphs." Scholarly Commons, 2021. https://scholarlycommons.pacific.edu/uop_etds/3730.

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Functional analyses (FAs) are an important component of treatment and the data gathered from FAs are often graphed in an aggregate or summary format, such as mean rate per session. Given the prevalence of undifferentiated analyses, it may be that this common method of data depiction is incomplete. In this paper, we compare the traditional aggregate method to a comprehensive second-by-second demonstration of the data including all appropriate and inappropriate responses emitted, as well as programmed and accidental antecedent and consequent variables, which may help further clarify the results of a functional analysis. We compared the functional analysis results of two participants when the data were depicted using the traditional rate aggregate method and depicted using a comprehensive second-by-second method. Although both rate and comprehensive second-by-second data depiction resulted in similar conclusions regarding the maintaining variables for the participants, comprehensive second-by-second data depiction allowed us to draw the conclusions in less time. Additional advantages and disadvantages of each method as it relates to efficiency, therapeutic risk and safety, and practicality are also discussed. Keywords: efficiency, functional analysis, problem behavior, safety, within-session second-by-second analysis.
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Books on the topic "Aggregated data"

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Ferguson, Walter L. Pesticide use on selected crops: Aggregated data, 1977-80. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, 1985.

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Ferguson, Walter L. Pesticide use on selected crops: Aggregated data, 1977-80. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, 1985.

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United States. Dept. of Agriculture. Economic Research Service., ed. Pesticide use on selected crops: Aggregated data, 1977-80. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, 1985.

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Ferguson, Walter L. Pesticide use on selected crops: Aggregated data, 1977-80. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, 1985.

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Hildén, Jonatan, and Laura Koivunen-Niemi. Learn to Create a Visualization of Aggregated Time Data in Python With Data From ACEA (2020). 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529605211.

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Adamson, Christopher. Mastering Data Warehouse Aggregates. New York: John Wiley & Sons, Ltd., 2006.

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Bagnoli, Luca, and Massimo Cini, eds. La cooperazione sociale nell'area metropolitana fiorentina. Florence: Firenze University Press, 2009. http://dx.doi.org/10.36253/978-88-8453-415-6.

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This study proposes a reading of the financial statements of co-operatives operating within the Florentine metropolitan area, with a view to underscoring business aspects of a quantitative and monetary nature. The entrepreneurial nature of the co-operatives is taken as read, focusing as with any other corporate entity on assets and economic results as indicators of success. The financial statements were collected from co-operatives with their registered offices in the provinces of Florence, Prato and Pistoia for the financial years 2004-2007, and the accounts were then aggregated, reclassified and analysed. The observations on the resulting data are then broken down by province and by type of co-operative (A or B).
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Bayoumi, Tamim A. Estimating trade equations from aggregate bilateral data. [Washington, D.C.]: International Monetary Fund, Asia and Pacific Department, 1999.

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Vasconcellos, Klaus Leite Pinto. Aspects of forecasting aggregate and discrete data. [s.l.]: typescript, 1992.

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FGBOU, VO. Digital analytics and financial security control of socially significant organizations. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1863937.

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The monograph is devoted to the formation of the concept of digital financial security analytics. The use of the digital environment and big data analysis tools in the system of monitoring sectoral risks and monitoring the activities of socially significant organizations from the position of the ESG strategy is disclosed. At the same time, financial security is considered as an aggregated result of the action of economic, environmental and social factors in a rapidly changing economy. It covers several key areas that make it possible to digitalize and improve the effectiveness of monitoring the activities of socially significant organizations in a complex: the development of the conceptual apparatus of socially significant business; analytical tools for assessing and forecasting financial security risks based on the concept of sustainable development; standardization of risk management. For students, postgraduates, teachers, as well as for the professional development of managerial personnel in business and government structures.
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Book chapters on the topic "Aggregated data"

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Zimmermann, Albrecht, and Björn Bringmann. "Aggregated Subset Mining." In Advances in Knowledge Discovery and Data Mining, 664–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_66.

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Chen, Qixin, Hongye Guo, Kedi Zheng, and Yi Wang. "Aggregated Supply Curves Forecasting." In Data Analytics in Power Markets, 211–38. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4975-2_11.

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Buchin, Kevin, Maike Buchin, Marc van Kreveld, Maarten Löffler, Jun Luo, and Rodrigo I. Silveira. "Clusters in Aggregated Health Data." In Headway in Spatial Data Handling, 77–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68566-1_5.

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Huo, Xiaoming, Cheng Huang, and Xuelei Sherry Ni. "Scattered Data and Aggregated Inference." In Handbook of Big Data Analytics, 75–102. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-18284-1_4.

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Acs, Gergely, Szilvia Lestyán, and Gergely Biczók. "Privacy of Aggregated Mobility Data." In Encyclopedia of Cryptography, Security and Privacy, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-642-27739-9_1575-1.

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Wang, Yi, Qixin Chen, and Chongqing Kang. "Aggregated Load Forecasting with Sub-profiles." In Smart Meter Data Analytics, 271–85. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2624-4_12.

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Aimoto, Yoshifumi, and Hisashi Kashima. "Matrix Factorization With Aggregated Observations." In Advances in Knowledge Discovery and Data Mining, 521–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37456-2_44.

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Bayliss, David. "Aggregated Data Analysis in HPCC Systems." In Big Data Technologies and Applications, 225–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44550-2_8.

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Wojtusiak, Janusz, and Ancha Baranova. "Model Learning from Published Aggregated Data." In Learning Structure and Schemas from Documents, 369–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22913-8_17.

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Canonne, Clément, and Ronitt Rubinfeld. "Testing Probability Distributions Underlying Aggregated Data." In Automata, Languages, and Programming, 283–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43948-7_24.

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Conference papers on the topic "Aggregated data"

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Bax, Eric, and Charlotte Bax. "SAFE – Secure Aggregated Frequency Estimates." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378135.

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Andrews, Keith, Majda Osmić, and Gerhard Schagerl. "Aggregated parallel coordinates." In i-KNOW '15: 15th International Conference on Knowledge Technologies and Data-Driven Business. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2809563.2809588.

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Wallner, Günter, Nour Halabi, and Pejman Mirza-Babaei. "Aggregated Visualization of Playtesting Data." In CHI '19: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3290605.3300593.

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Rubin, Andee. "Using Data for Good: A Matter of Geography." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t2i2.

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The purpose of this research is to learn how youth aged 11–14 understand highly aggregated data about social and economic conditions, especially related to the United Nations Sustainable Development Goals. We designed an after-school program that introduces students to data broadly in the context of “using data for social good.” Using CODAP, an educational data visualization tool, students explore data about countries’ health and education indicators. We observe that youth are highly engaged with these data yet sometimes struggle to make sense of the aggregate values that hide variability within countries. Using examples from student projects, this paper reports preliminary findings about how youth analyze data aggregated by governmental entities such as countries, states, or cities.
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Kristiansen, Raymond, and Chris Petrich. "Extracting human capital from aggregated data." In 2016 International Symposium on Small-scale Intelligent Manufacturing Systems (SIMS). IEEE, 2016. http://dx.doi.org/10.1109/sims.2016.7802898.

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Kaaniche, Nesrine, Eunjin Jung, and Ashish Gehani. "Efficiently Validating Aggregated IoT Data Integrity." In 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService). IEEE, 2018. http://dx.doi.org/10.1109/bigdataservice.2018.00046.

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"AGGREGATED ACCOUNTING OF MEMORY USAGE IN JAVA." In 4th International Conference on Software and Data Technologies. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002253701770185.

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Jung, Soon-Gyo, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, and Bernard J. Jansen. "Persona Generation from Aggregated Social Media Data." In CHI '17: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3027063.3053120.

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Mikhalskii, A. I., V. P. Gorlischev, D. A. Jdanov, and P. Grigoriev. "Splitting of aggregated medical and demographic data." In 2017 IEEE 11th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2017. http://dx.doi.org/10.1109/icaict.2017.8686994.

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Woodbridge, Jonathan, Bobak Mortazavi, Majid Sarrafzadeh, and Alex A. T. Bui. "Aggregated Indexing of Biomedical Time Series Data." In 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB). IEEE, 2012. http://dx.doi.org/10.1109/hisb.2012.13.

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Reports on the topic "Aggregated data"

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Breza, Emily, Arun Chandrasekhar, Tyler McCormick, and Mengjie Pan. Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data. Cambridge, MA: National Bureau of Economic Research, June 2017. http://dx.doi.org/10.3386/w23491.

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Garrett, Thomas A. Aggregated vs. Disaggregated Data in Regression Analysis: Implications for Inference. Federal Reserve Bank of St. Louis, 2002. http://dx.doi.org/10.20955/wp.2002.024.

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van der Sloot, Bart. The Quality of Life: Protecting Non-personal Interests and Non-personal Data in the Age of Big Data. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64579.

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Under the current legal paradigm, the rights to privacy and data protection provide natural persons with subjective rights to protect their private interests, such as related to human dignity, individual autonomy and personal freedom. In principle, when data processing is based on non-personal or aggregated data or when such data pro- cesses have an impact on societal, rather than individual interests, citizens cannot rely on these rights. Although this legal paradigm has worked well for decades, it is increasingly put under pressure because Big Data processes are typically based indis- criminate rather than targeted data collection, because the high volumes of data are processed on an aggregated rather than a personal level and because the policies and decisions based on the statistical correlations found through algorithmic analytics are mostly addressed at large groups or society as a whole rather than specific individuals. This means that large parts of the data-driven environment are currently left unregu- lated and that individuals are often unable to rely on their fundamental rights when addressing the more systemic effects of Big Data processes. This article will discuss how this tension might be relieved by turning to the notion ‘quality of life’, which has the potential of becoming the new standard for the European Court of Human Rights (ECtHR) when dealing with privacy related cases.
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Bond, W., Maria Seale, and Jeffrey Hensley. A dynamic hyperbolic surface model for responsive data mining. Engineer Research and Development Center (U.S.), April 2022. http://dx.doi.org/10.21079/11681/43886.

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Data management systems impose structure on data via a static representation schema or data structure. Information from the data is extracted by executing queries based on predefined operators. This paradigm restricts the searchability of the data to concepts and relationships that are known or assumed to exist among the objects. While this is an effective and efficient means of retrieving simple information, we propose that such a structure severely limits the ability to derive breakthrough knowledge that exists in data under the guise of “unknown unknowns.” A dynamic system will alleviate this dependence, allowing theoretically infinite projections of the data to reveal discoverable relationships that are hidden by traditional use case-driven, static query systems. In this paper, we propose a framework for a data-responsive query algebra based on a dynamic hyperbolic surface model. Such a model could provide more intuitive access to analytics and insights from massive, aggregated datasets than existing methods. This model will significantly alter the means of addressing the underlying data by representing it as an arrangement on a dynamic, hyperbolic plane. Consequently, querying the data can be viewed as a process similar to quantum annealing, in terms of characterizing data representation as an energy minimization problem with numerous minima.
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Snyder, Victor A., Dani Or, Amos Hadas, and S. Assouline. Characterization of Post-Tillage Soil Fragmentation and Rejoining Affecting Soil Pore Space Evolution and Transport Properties. United States Department of Agriculture, April 2002. http://dx.doi.org/10.32747/2002.7580670.bard.

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Tillage modifies soil structure, altering conditions for plant growth and transport processes through the soil. However, the resulting loose structure is unstable and susceptible to collapse due to aggregate fragmentation during wetting and drying cycles, and coalescense of moist aggregates by internal capillary forces and external compactive stresses. Presently, limited understanding of these complex processes often leads to consideration of the soil plow layer as a static porous medium. With the purpose of filling some of this knowledge gap, the objectives of this Project were to: 1) Identify and quantify the major factors causing breakdown of primary soil fragments produced by tillage into smaller secondary fragments; 2) Identify and quantify the. physical processes involved in the coalescence of primary and secondary fragments and surfaces of weakness; 3) Measure temporal changes in pore-size distributions and hydraulic properties of reconstructed aggregate beds as a function of specified initial conditions and wetting/drying events; and 4) Construct a process-based model of post-tillage changes in soil structural and hydraulic properties of the plow layer and validate it against field experiments. A dynamic theory of capillary-driven plastic deformation of adjoining aggregates was developed, where instantaneous rate of change in geometry of aggregates and inter-aggregate pores was related to current geometry of the solid-gas-liquid system and measured soil rheological functions. The theory and supporting data showed that consolidation of aggregate beds is largely an event-driven process, restricted to a fairly narrow range of soil water contents where capillary suction is great enough to generate coalescence but where soil mechanical strength is still low enough to allow plastic deforn1ation of aggregates. The theory was also used to explain effects of transient external loading on compaction of aggregate beds. A stochastic forInalism was developed for modeling soil pore space evolution, based on the Fokker Planck equation (FPE). Analytical solutions for the FPE were developed, with parameters which can be measured empirically or related to the mechanistic aggregate deformation model. Pre-existing results from field experiments were used to illustrate how the FPE formalism can be applied to field data. Fragmentation of soil clods after tillage was observed to be an event-driven (as opposed to continuous) process that occurred only during wetting, and only as clods approached the saturation point. The major mechanism of fragmentation of large aggregates seemed to be differential soil swelling behind the wetting front. Aggregate "explosion" due to air entrapment seemed limited to small aggregates wetted simultaneously over their entire surface. Breakdown of large aggregates from 11 clay soils during successive wetting and drying cycles produced fragment size distributions which differed primarily by a scale factor l (essentially equivalent to the Van Bavel mean weight diameter), so that evolution of fragment size distributions could be modeled in terms of changes in l. For a given number of wetting and drying cycles, l decreased systematically with increasing plasticity index. When air-dry soil clods were slightly weakened by a single wetting event, and then allowed to "age" for six weeks at constant high water content, drop-shatter resistance in aged relative to non-aged clods was found to increase in proportion to plasticity index. This seemed consistent with the rheological model, which predicts faster plastic coalescence around small voids and sharp cracks (with resulting soil strengthening) in soils with low resistance to plastic yield and flow. A new theory of crack growth in "idealized" elastoplastic materials was formulated, with potential application to soil fracture phenomena. The theory was preliminarily (and successfully) tested using carbon steel, a ductile material which closely approximates ideal elastoplastic behavior, and for which the necessary fracture data existed in the literature.
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Cavalli, Nicolò. Future orientation and fertility: cross-national evidence using Google search. Verlag der Österreichischen Akademie der Wissenschaften, December 2020. http://dx.doi.org/10.1553/populationyearbook2020.res06.

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Using digital traces to investigate demographic behaviours, I leverage in this paper aggregated web search data to develop a Future Orientation Index for 200 countries and territories across the world. This index is expressed as the ratio of Google search volumes for ‘next year’ (e.g., 2021) to search volumes for ‘current year’ (e.g., 2020), adjusted for country-level internet penetration rates. I show that countries with lower levels of future orientation also have higher levels of fertility. Fertility rates decrease quickly as future orientation levels increase; but at the highest levels of future orientation, this correlation flattens out. Theoretically, I reconstruct the role that varying degrees of future orientation might play in fertility decisions by incorporating advances in behavioural economics into a traditional quantity-quality framework à la Becker.
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Beaulieu, J. Joseph, and Jeffrey Miron. Seasonal Unit Roots in Aggregate U.S. Data. Cambridge, MA: National Bureau of Economic Research, August 1992. http://dx.doi.org/10.3386/t0126.

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Vecherin, Sergey, Derek Chang, Emily Wells, Benjamin Trump, Aaron Meyer, Jacob Desmond, Kyle Dunn, Maxim Kitsak, and Igor Linkov. Assessment of the COVID-19 infection risk at a workplace through stochastic microexposure modeling. Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43740.

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The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks. This paper describes a novel model for COVID-19 infection risks and policy evaluations. The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific interagent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies. The application of the model is demonstrated for a typical office environment and for a real-world case. The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.
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Petrin, Amil, and James Levinsohn. Measuring Aggregate Productivity Growth Using Plant-Level Data. Cambridge, MA: National Bureau of Economic Research, December 2005. http://dx.doi.org/10.3386/w11887.

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Bachmann, Rüdiger, and Peter Zorn. What Drives Aggregate Investment? Evidence from German Survey Data. Cambridge, MA: National Bureau of Economic Research, April 2013. http://dx.doi.org/10.3386/w18990.

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