Articles de revues : « Understanding of data models »

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Shanks, Graeme, et Peta Darke. « Understanding corporate data models ». Information & ; Management 35, n^o 1 (janvier 1999) : 19–30. http://dx.doi.org/10.1016/s0378-7206(98)00078-0.

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French, Robert M., et Maud Jacquet. « Understanding bilingual memory : models and data ». Trends in Cognitive Sciences 8, n^o 2 (février 2004) : 87–93. http://dx.doi.org/10.1016/j.tics.2003.12.011.

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DeBruine, Lisa M., et Dale J. Barr. « Understanding Mixed-Effects Models Through Data Simulation ». Advances in Methods and Practices in Psychological Science 4, n^o 1 (janvier 2021) : 251524592096511. http://dx.doi.org/10.1177/2515245920965119.

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Experimental designs that sample both subjects and stimuli from a larger population need to account for random effects of both subjects and stimuli using mixed-effects models. However, much of this research is analyzed using analysis of variance on aggregated responses because researchers are not confident specifying and interpreting mixed-effects models. This Tutorial explains how to simulate data with random-effects structure and analyze the data using linear mixed-effects regression (with the lme4 R package), with a focus on interpreting the output in light of the simulated parameters. Data simulation not only can enhance understanding of how these models work, but also enables researchers to perform power calculations for complex designs. All materials associated with this article can be accessed at https://osf.io/3cz2e/ .

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Knüsel, Benedikt, et Christoph Baumberger. « Understanding climate phenomena with data-driven models ». Studies in History and Philosophy of Science Part A 84 (décembre 2020) : 46–56. http://dx.doi.org/10.1016/j.shpsa.2020.08.003.

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Durant, Szonya. « Zhaoping, L. Understanding Vision : Theory, Models, and Data ». Perception 45, n^o 10 (19 juillet 2016) : 1207–8. http://dx.doi.org/10.1177/0301006616660638.

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Best, Nicky, et Peter Green. « Structure and uncertainty : Graphical models for understanding complex data ». Significance 2, n^o 4 (30 novembre 2005) : 177–81. http://dx.doi.org/10.1111/j.1740-9713.2005.00133.x.

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Steinberg, David M., et Dizza Bursztyn. « Data Analytic Tools for Understanding Random Field Regression Models ». Technometrics 46, n^o 4 (novembre 2004) : 411–20. http://dx.doi.org/10.1198/004017004000000419.

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Cagetti, Marco, et Mariacristina De Nardi. « WEALTH INEQUALITY : DATA AND MODELS ». Macroeconomic Dynamics 12, S2 (septembre 2008) : 285–313. http://dx.doi.org/10.1017/s1365100507070150.

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In the United States wealth is highly concentrated and very unequally distributed: the richest 1% hold one third of the total wealth in the economy. Understanding the determinants of wealth inequality is a challenge for many economic models. We summarize some key facts about the wealth distribution and what economic models have been able to explain so far.

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Butts, Daniel A. « Data-Driven Approaches to Understanding Visual Neuron Activity ». Annual Review of Vision Science 5, n^o 1 (15 septembre 2019) : 451–77. http://dx.doi.org/10.1146/annurev-vision-091718-014731.

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With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.

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Yoo, Kang Min, Youhyun Shin et Sang-goo Lee. « Data Augmentation for Spoken Language Understanding via Joint Variational Generation ». Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 juillet 2019) : 7402–9. http://dx.doi.org/10.1609/aaai.v33i01.33017402.

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Data scarcity is one of the main obstacles of domain adaptation in spoken language understanding (SLU) due to the high cost of creating manually tagged SLU datasets. Recent works in neural text generative models, particularly latent variable models such as variational autoencoder (VAE), have shown promising results in regards to generating plausible and natural sentences. In this paper, we propose a novel generative architecture which leverages the generative power of latent variable models to jointly synthesize fully annotated utterances. Our experiments show that existing SLU models trained on the additional synthetic examples achieve performance gains. Our approach not only helps alleviate the data scarcity issue in the SLU task for many datasets but also indiscriminately improves language understanding performances for various SLU models, supported by extensive experiments and rigorous statistical testing.

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Alla, Alessandro, Caterina Balzotti, Maya Briani et Emiliano Cristiani. « Understanding Mass Transfer Directions via Data-Driven Models with Application to Mobile Phone Data ». SIAM Journal on Applied Dynamical Systems 19, n^o 2 (janvier 2020) : 1372–91. http://dx.doi.org/10.1137/19m1248479.

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Mara, Constance A., et Adam C. Carle. « Understanding Variation in Longitudinal Data Using Latent Growth Mixture Modeling ». Journal of Pediatric Psychology 46, n^o 2 (18 février 2021) : 179–88. http://dx.doi.org/10.1093/jpepsy/jsab010.

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Abstract Objective This article guides researchers through the process of specifying, troubleshooting, evaluating, and interpreting latent growth mixture models. Methods Latent growth mixture models are conducted with small example dataset of N = 117 pediatric patients using Mplus software. Results The example and data show how to select a solution, here a 3-class solution. We also present information on two methods for incorporating covariates into these models. Conclusions Many studies in pediatric psychology seek to understand how an outcome changes over time. Mixed models or latent growth models estimate a single average trajectory estimate and an overall estimate of the individual variability, but this may mask other patterns of change shared by some participants. Unexplored variation in longitudinal data means that researchers can miss critical information about the trajectories of subgroups of individuals that could have important clinical implications about how one assess, treats, and manages subsets of individuals. Latent growth mixture modeling is a method for uncovering subgroups (or “classes”) of individuals with shared trajectories that differ from the average trajectory.

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Quick, Corbin, Rounak Dey et Xihong Lin. « Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data ». Journal of the American Statistical Association 116, n^o 536 (2 octobre 2021) : 1561–77. http://dx.doi.org/10.1080/01621459.2021.2001339.

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Corrales-Garay, Diego, Marta Ortiz-de-Urbina-Criado et Eva-María Mora-Valentín. « Understanding open data business models from innovation and knowledge management perspectives ». Business Process Management Journal 28, n^o 2 (14 mars 2022) : 532–54. http://dx.doi.org/10.1108/bpmj-06-2021-0373.

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PurposeThis paper aims to analyse the open data business models (ODBMs) as a source of knowledge and innovation to generate economic and social value. A framework for understanding ODBMs is presented. First, the knowledge structure of the ODBMs literature is identified. Second, a conceptual model for analysing the ODBMs is proposed. And finally, the future trends in ODBMs research are discussed.Design/methodology/approachIn this paper, co-word analysis is performed to identify the topics related with ODBMs.FindingsThe ODBMs structure of knowledge is articulated in five themes: business model, smart city, business ecosystem, decision-making and innovation. Based on these results, a five-step model for analysing ODBMs is proposed. Finally, a discussion of the future trends of ODBMs focussed on a knowledge management perspective, open data ecosystems and business intelligence is presented.Originality/valueThe paper presents a picture of what, where, for whom and for what ODBMs have been studied and develops a new model to explain the value creation process of ODBMs. Taking a step further, applying the principles and models of knowledge management and business intelligence to ODBMs is also recommended in order to transfer and transform open data into valuable knowledge that can be used for developing apps. In that context, the importance of encouraging collaboration between different agents in the so-called open data ecosystem is presented.

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Wang, Yan, et Lewi Stone. « Understanding the connections between species distribution models for presence-background data ». Theoretical Ecology 12, n^o 1 (1 septembre 2018) : 73–88. http://dx.doi.org/10.1007/s12080-018-0389-9.

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McLaren, Sandra, W. James Dunlap et Roger Powell. « Understanding K-feldspar 40Ar/39Ar data : reconciling models, methods and microtextures ». Journal of the Geological Society 164, n^o 5 (septembre 2007) : 941–44. http://dx.doi.org/10.1144/0016-76492006-180.

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Poulter, Benjamin. « Mixing ecological models and data for understanding long-term ecosystem dynamics ». Ecology 97, n^o 10 (octobre 2016) : 2893–94. http://dx.doi.org/10.1002/ecy.1492.

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Gill, Alastair, et Francisco Iacobelli. « Understanding Socially Constructed Concepts Using Blogs Data ». Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 (23 septembre 2015) : 8–9. http://dx.doi.org/10.1609/hcomp.v3i1.13250.

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In this paper we propose a methodology to understand complex concepts, and which captures aspects of the contextual —and collaboratively constructed — meaning of these concepts with considerably less effort than manual coding. We use the word "quality" as one such concept to exemplify our methodology. By using unsupervised topic models along with a small corpus of human labeled data we explore the different uses of the concept "quality" in a large number of blogs. Our methodology is validated, qualitatively, by comparing our results to previous research. Finally, we note limitations and future directions of this work.

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Plotnick, Roy E. « Creating Models for Interpreting Data ». Paleontological Society Special Publications 11 (2002) : 275–88. http://dx.doi.org/10.1017/s2475262200009989.

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For many of us, the word model may trigger remembrances of long-ago afternoons spent painstakingly gluing a small plastic car or airplane model together (for others, it may conjure up images of a somewhat emaciated young woman staring out of the cover of Vogue or Elle). The model car is not the same as a real car; it is made of different materials, it has many fewer parts, and it does not move. Nevertheless, it resembles a real automobile sufficiently that we recognize it as a realistic representation. Similarly, scientists use the term model to refer to a reconstruction of nature for the purpose of study (Levins, 1966). In other words, in order to understand nature, one may not always want to study it directly. Instead, understanding may come from studying a facsimile of nature that captures what is perceived to be its essential properties. In the same way, a child may learn how a car is built by building a plastic model of it, even if this model contains no moving parts.

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Plotnick, Roy E. « Creating Models for Interpreting Data ». Paleontological Society Special Publications 9 (1999) : 343–58. http://dx.doi.org/10.1017/s2475262200014180.

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For many of us, the word model may trigger remembrances of long-ago afternoons spent painstakingly gluing a small plastic car or airplane model together (for others, it may conjure up images of a somewhat emaciated young woman staring out of the cover of Vogue or Elle). The model car is not the same as a real car; it is made of different materials; it has many fewer parts, and it does not move. Nevertheless, it resembles a real automobile sufficiently that we recognize it as a realistic representation. Similarly, scientists use the term model to refer to a reconstruction of nature for the purpose of study (Levins, 1966). In other words, in order to understand nature, one may not always want to study it directly. Instead, understanding may come from studying a facsimile of nature that captures what is perceived to be its essential properties. In the same way, a child may learn how a car is built by building a plastic model of it, even if this model contains no moving parts.

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Cajias, Marcelo. « Understanding real estate investments through big data goggles ». International Journal of Housing Markets and Analysis 12, n^o 4 (5 août 2019) : 661–86. http://dx.doi.org/10.1108/ijhma-06-2018-0042.

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Purpose This paper aims to develop a conceptual understanding and a methodological approach for calculating residential net initial yields for both a buy-to-hold and rental investment strategy from hedonic models. Design/methodology/approach The markets modelled comprehend of dwellings for rent and sell in Germany. For each of them, two regression models are estimated to extract implicit prices and rents for an artificial identical dwelling and estimate the willingness to pay for the same asset from both a buy-to-hold and rental investment strategy. Findings The 3,381 estimated net initial yields in the 161 German markets showed a spatial pattern with the biggest and most attractive cities showing the lowest yields and a self-adjusting process in the markets surrounding the top cities. The net initial yields over time show that prices have increased stronger than rents, leading to rock bottom yields for residential assets and a significant premium in comparison to government bond yields. The approach responds to the spatial hierarchy of markets in Germany, meaning that the level of the estimated yields is accurate and achievable from an investment perspective. Practical implications The investment case in residential markets is certainly unique as net initial yields are scarce, especially due to the relatively low number of investment comparables. The paper sheds light on this problem from a conceptual and methodological perspective and confirms that investment yields are deducible by making usage of hedonic models and big data. Originality/value In the era of digitalization and big data, residential assets are mostly brought to the market via digital multiple listing systems. Transparency is an essential barrier when assessing the pricing conditions of markets and deriving investment decisions. Although international brokers do provide detailed investment comparables on – mostly commercial – real estate markets, the residential sector remains a puzzle when it comes to investment yields. The paper sheds light on this problem.

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Narayana, Pradyumna, J. Ross Beveridge et Bruce A. Draper. « Interacting Hidden Markov Models for Video Understanding ». International Journal of Pattern Recognition and Artificial Intelligence 32, n^o 11 (24 juillet 2018) : 1855020. http://dx.doi.org/10.1142/s0218001418550200.

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People, cars and other moving objects in videos generate time series data that can be labeled in many ways. For example, classifiers can label motion tracks according to the object type, the action being performed, or the trajectory of the motion. These labels can be generated for every frame as long as the object stays in view, so object tracks can be modeled as Markov processes with multiple noisy observation streams. A challenge in video recognition is to recover the true state of the track (i.e. its class, action and trajectory) using Markov models without (a) counter-factually assuming that the streams are independent or (b) creating a fully coupled Hidden Markov Model (FCHMM) with an infeasibly large state space. This paper introduces a new method for labeling sequences of hidden states. The method exploits external consistency constraints among streams without modeling complex joint distributions between them. For example, common sense semantics suggest that trees cannot walk. This is an example of an external constraint between an object label (“tree”) and an action label (“walk”). The key to exploiting external constraints is a new variation of the Viterbi algorithm which we call the Viterbi–Segre (VS) algorithm. VS restricts the solution spaces of factorized HMMs to marginal distributions that are compatible with joint distributions satisfying sets of external constraints. Experiments on synthetic data show that VS does a better job of estimating true states with the given observations than the traditional Viterbi algorithm applied to (a) factorized HMMs, (b) FCHMMs, or (c) partially-coupled HMMs that model pairwise dependencies. We then show that VS outperforms factorized and pairwise HMMs on real video data sets for which FCHMMs cannot feasibly be trained.

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Pudney, Stephen. « intcount : A command for fitting count-data models from interval data ». Stata Journal : Promoting communications on statistics and Stata 19, n^o 3 (septembre 2019) : 645–66. http://dx.doi.org/10.1177/1536867x19874240.

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In this article, I describe a community-contributed command, intcount, that fits one of several regression models for count data observed in interval form. The models available are Poisson, negative binomial, and binomial, and they can be fit in standard or zero-inflated form. I illustrate the command with an application to analysis of data from the UK Understanding Society survey on the demand for healthcare services.

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Osuntoki, Seun, Victor Odumuyiwa et Oladipupo Sennaike. « Understanding Document Thematic Structure ». Journal of information and organizational sciences 46, n^o 2 (22 décembre 2022) : 305–22. http://dx.doi.org/10.31341/jios.46.2.3.

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The increasing usage of the Internet and other digital platforms has brought in the eraof big data with the attending increase in the quantity of unstructured data that isavailable for processing and storage. However, the full benefits of analyzing this largequantity of unstructured data will not be realized without proper techniques andalgorithms. Topic modeling algorithms have seen a major success in this area. Differenttopic modeling algorithms exist and each one either employs probabilistic or linearalgebra approaches. Recent reviews on topic modeling algorithms dwell majorly onprobabilistic methods without giving proper treatment to the linear-algebra-basedalgorithms. This review explores linear-algebra-based topic models as well asprobability-based topic models. An overview of how models generated by each of thesealgorithms represent document thematic structure is also presented

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Fisac, Ramon, et Ana Moreno-Romero. « Understanding social enterprise country models : Spain ». Social Enterprise Journal 11, n^o 2 (3 août 2015) : 156–77. http://dx.doi.org/10.1108/sej-02-2014-0012.

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Purpose – The purpose of this paper is to describe the historical institutional context of Spain in the past 40 years and to analyze the influence of institutional factors in the current model of social enterprise existing in the country. Design/methodology/approach – This study draws on the theory of historical institutionalism, national-level empirical data and Kerlin conceptual framework (2013) that informs models of social enterprise. Findings – This paper describe some traits of Spain’s social enterprise that can be explained by the evolution of its institutional context in the past 40 years. It helps to validate, from a historical institutionalistic perspective, aspects of the Kerlin framework for social enterprise models. It also begins to show that the analysis of regional differences in the context should be taken into consideration when examining a country’s social enterprise space. Research limitations/implications – This discussion paper encourages academics to analyze regional differences in the emergence of social enterprise within a country. The main limitation of the paper is the lack of an “official” definition of social enterprise in Spain. Originality/value – This paper applies a valuable framework to a country with a unique political and economic history in the past 40 years. It contributes to enrich the research on the emergence and development of social enterprises in a variety of contexts and advances understanding of how regional differences inside a country influence the development of social enterprises.

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Santos‐Fernandez, Edgar, et Kerrie Mengersen. « Understanding the reliability of citizen science observational data using item response models ». Methods in Ecology and Evolution 12, n^o 8 (19 mai 2021) : 1533–48. http://dx.doi.org/10.1111/2041-210x.13623.

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Quick, Corbin, Rounak Dey et Xihong Lin. « Rejoinder : Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data ». Journal of the American Statistical Association 116, n^o 536 (2 octobre 2021) : 1591–94. http://dx.doi.org/10.1080/01621459.2021.2001340.

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Demurjian, S. A., et D. K. Hsiao. « Towards a better understanding of data models through the multilingual database system ». IEEE Transactions on Software Engineering 14, n^o 7 (juillet 1988) : 946–58. http://dx.doi.org/10.1109/32.42737.

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Ramachandran, Sai Niranjan, Rudrabha Mukhopadhyay, Madhav Agarwal, C. V. Jawahar et Vinay Namboodiri. « Understanding the Generalization of Pretrained Diffusion Models on Out-of-Distribution Data ». Proceedings of the AAAI Conference on Artificial Intelligence 38, n^o 13 (24 mars 2024) : 14767–75. http://dx.doi.org/10.1609/aaai.v38i13.29395.

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This work tackles the important task of understanding out-of-distribution behavior in two prominent types of generative models, i.e., GANs and Diffusion models. Understanding this behavior is crucial in understanding their broader utility and risks as these systems are increasingly deployed in our daily lives. Our first contribution is demonstrating that diffusion spaces outperform GANs' latent spaces in inverting high-quality OOD images. We also provide a theoretical analysis attributing this to the lack of prior holes in diffusion spaces. Our second significant contribution is to provide a theoretical hypothesis that diffusion spaces can be projected onto a bounded hypersphere, enabling image manipulation through geodesic traversal between inverted images. Our analysis shows that different geodesics share common attributes for the same manipulation, which we leverage to perform various image manipulations. We conduct thorough empirical evaluations to support and validate our claims. Finally, our third and final contribution introduces a novel approach to the few-shot sampling for out-of-distribution data by inverting a few images to sample from the cluster formed by the inverted latents. The proposed technique achieves state-of-the-art results for the few-shot generation task in terms of image quality. Our research underscores the promise of diffusion spaces in out-of-distribution imaging and offers avenues for further exploration. Please find more details about our project at \url{http://cvit.iiit.ac.in/research/projects/cvit-projects/diffusionOOD}

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Ma, Jiao, et Colin G. Drury. « Analysis of Collaborative Meetings in Developing Data Mining Models ». Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, n^o 5 (septembre 2005) : 686–89. http://dx.doi.org/10.1177/154193120504900511.

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Observations of group meetings were used to help our understanding of the Data Mining (DM) process, which can take a year to complete. Over a course of three months, we followed two collaborative groups and observed their weekly meetings, where they devised DM models and explored new ways to analyze and present microarray data. The study furthered the current understanding of the DM activities by revealing its socio-technical aspects, and directed a promising design approach for a more efficient and effective DM system. Field observations of collaborative meetings disclosed that a longitudinal study is, in fact, appropriate and necessary to further understand the DM process and the system.

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Max, Ludo, et Elana M. Yudman. « Understanding Stuttering Will Require Theoretical Models That Fit the Data Rather Than Attempts to Make the Data Fit the Preferred Models ». Journal of Speech, Language, and Hearing Research 47, n^o 1 (février 2004) : 105–13. http://dx.doi.org/10.1044/1092-4388(2004/010).

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Frazier, Christopher, et Kara M. Kockelman. « Spatial Econometric Models for Panel Data ». Transportation Research Record : Journal of the Transportation Research Board 1902, n^o 1 (janvier 2005) : 80–90. http://dx.doi.org/10.1177/0361198105190200110.

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Cities are constantly evolving, complex systems, and modeling them, both theoretically and empirically, is a complicated task. However, understanding the manner in which developed regions change over time and space can be important for transportation researchers and planners. In this paper, methodologies for modeling developed areas are presented, and spatial and temporal effects of the data are incorporated into the methodologies. The work emphasizes spatial relationships between various geographic, land use, and demographic variables that characterize fine zones across regions. It derives and combines land cover data for the Austin, Texas, region from a panel of satellite images and U.S. Census of Population data. Models for population, vehicle ownership, and developed, residential, and agricultural land cover are estimated; the effects of space and time on the models are shown to be statistically significant. Simulations of population and land cover for the year 2020 help to illustrate the strengths and limitations of the models.

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Ghorbani, Amir, Vahid Ghorbani, Morteza Nazari-Heris et Somayeh Asadi. « Data Assimilation for Agent-Based Models ». Mathematics 11, n^o 20 (15 octobre 2023) : 4296. http://dx.doi.org/10.3390/math11204296.

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This article presents a comprehensive review of the existing literature on the topic of data assimilation for agent-based models, with a specific emphasis on pedestrians and passengers within the context of transportation systems. This work highlights a plethora of advanced techniques that may have not been previously employed for online pedestrian simulation, and may therefore offer significant value to readers in this domain. Notably, these methods often necessitate a sophisticated understanding of mathematical principles such as linear algebra, probability theory, singular value decomposition, optimization, machine learning, and compressed sensing. Despite this complexity, this article strives to provide a nuanced explanation of these mathematical underpinnings. It is important to acknowledge that the subject matter under study is still in its nascent stages, and as such, it is highly probable that new techniques will emerge in the coming years. One potential avenue for future exploration involves the integration of machine learning with Agent-based Data Assimilation (ABDA, i.e., data assimilation methods used for agent-based models) methods.

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Jefferson, Philip N. « Deriving the GLS Transformation Parameter in Elementary Panel Data Models ». American Economist 49, n^o 1 (mars 2005) : 45–48. http://dx.doi.org/10.1177/056943450504900103.

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The Generalized Least Squares (GLS) transformation that eliminates serial correlation in the error terms is central to a complete understanding of the relationship between the pooled OLS, random effects, and fixed effects estimators. A significant hurdle to attainment of that understanding is the calculation of the parameter that delivers the desired transformation. This paper derives this critical parameter in the benchmark case typically used to introduce these estimators using nothing more than elementary statistics (mean, variance, and covariance) and the quadratic formula.

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Miller, George R., et Richard L. Burger. « Ch’arki at Chavín : Ethnographic Models and Archaeological Data ». American Antiquity 65, n^o 3 (juillet 2000) : 573–76. http://dx.doi.org/10.2307/2694537.

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We welcome Valdez"s recent contributions to the developing corpus of ethnographic observations concerning the production and use of Ch’arki in the Andes and to our understanding of the interpretative value of differential camelid bone concentrations in Andean archaeological sites. The numerous issues raised by Valdez would require more space than is available in this forum to adequately address them. What we will present here can only hope to outline the most salient points of contention and encourage further investigations into these problems.

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Sawada, Yohei, et Risa Hanazaki. « Socio-hydrological data assimilation : analyzing human–flood interactions by model–data integration ». Hydrology and Earth System Sciences 24, n^o 10 (5 octobre 2020) : 4777–91. http://dx.doi.org/10.5194/hess-24-4777-2020.

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Abstract. In socio-hydrology, human–water interactions are simulated by mathematical models. Although the integration of these socio-hydrological models and observation data is necessary for improving the understanding of human–water interactions, the methodological development of the model–data integration in socio-hydrology is in its infancy. Here we propose applying sequential data assimilation, which has been widely used in geoscience, to a socio-hydrological model. We developed particle filtering for a widely adopted flood risk model and performed an idealized observation system simulation experiment and a real data experiment to demonstrate the potential of the sequential data assimilation in socio-hydrology. In these experiments, the flood risk model's parameters, the input forcing data, and empirical social data were assumed to be somewhat imperfect. We tested if data assimilation can contribute to accurately reconstructing the historical human–flood interactions by integrating these imperfect models and imperfect and sparsely distributed data. Our results highlight that it is important to sequentially constrain both state variables and parameters when the input forcing is uncertain. Our proposed method can accurately estimate the model's unknown parameters – even if the true model parameter temporally varies. The small amount of empirical data can significantly improve the simulation skill of the flood risk model. Therefore, sequential data assimilation is useful for reconstructing historical socio-hydrological processes by the synergistic effect of models and data.

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Pečarič, Mirko. « Understanding Differences between Equal Public Governance Models ». Central European Public Administration Review 18, n^o 1 (23 avril 2020) : 69–88. http://dx.doi.org/10.17573/cepar.2020.1.04.

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Public administrations try to address changes in societies with various styles through various reforms based on different governance models, which are frequently transformed into domestic frames regardless of local specifics. The need for a tool with which the ideal types of governance models could be accommodated with national goals is, in times of increasing complexity, more and more relevant. As data as such are produced through numerous predispositions, the article proposes Ashby’s variety to capture the latter, through which it is possible to get closer to a successful administration of goals. On the other hand, Douglas’s grid and group model, Miles et al.’s organisational strategy, structure and process, and Hofstede’s cultural dimensions are used for the identification of needs. Even though public bodies are aware of the impact that culture/values has/have on models of public administration, countries base their decisions on it/them only indirectly. This article emphasises that certain values should be directly included in the governance models in accordance with their cultural backgrounds. The latter are always present in decisions’ predispositions (from which decisions obtain their frames and weights), and a successful administrator should not disregard them.

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Mount, N. J., C. W. Dawson et R. J. Abrahart. « Legitimising data-driven models : exemplification of a new data-driven mechanistic modelling framework ». Hydrology and Earth System Sciences 17, n^o 7 (17 juillet 2013) : 2827–43. http://dx.doi.org/10.5194/hess-17-2827-2013.

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Abstract. In this paper the difficult problem of how to legitimise data-driven hydrological models is addressed using an example of a simple artificial neural network modelling problem. Many data-driven models in hydrology have been criticised for their black-box characteristics, which prohibit adequate understanding of their mechanistic behaviour and restrict their wider heuristic value. In response, presented here is a new generic data-driven mechanistic modelling framework. The framework is significant because it incorporates an evaluation of the legitimacy of a data-driven model's internal modelling mechanism as a core element in the modelling process. The framework's value is demonstrated by two simple artificial neural network river forecasting scenarios. We develop a novel adaptation of first-order partial derivative, relative sensitivity analysis to enable each model's mechanistic legitimacy to be evaluated within the framework. The results demonstrate the limitations of standard, goodness-of-fit validation procedures by highlighting how the internal mechanisms of complex models that produce the best fit scores can have lower mechanistic legitimacy than simpler counterparts whose scores are only slightly inferior. Thus, our study directly tackles one of the key debates in data-driven, hydrological modelling: is it acceptable for our ends (i.e. model fit) to justify our means (i.e. the numerical basis by which that fit is achieved)?

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Buchholz, Maximilian, et Harald Bathelt. « Models of Regional Economic Development : Illustrations Using U.S. Data ». Zeitschrift für Wirtschaftsgeographie 65, n^o 1 (1 mars 2021) : 28–42. http://dx.doi.org/10.1515/zfw-2020-0040.

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Abstract Considering stagnating regional prosperity levels and growing inter-regional disparities in many economies, this paper appeals for a renewed research agenda to deepen our understanding of regional economic development. This is done by discussing different conceptual perspectives, their empirical applications and open questions and suggestions for future research. Conventional approaches view development as an outcome of and dependent upon local economic structure. That is, high regional performance is associated with specific regional industrial and human capital mixes. We argue that to deepen our understanding of the mechanisms that drive regional economic development it is helpful to apply a relational approach that pays attention to the networks between economic actors across different spatial scales, from local to global. These generate knowledge as well as access to technologies, resources and markets, thereby catalyzing income growth. To support regional policy agendas, it is further necessary to go beyond identifying regularities that structure development and engage with differing regional pathways by conducting systematic comparative analyses of local contextual and institutional conditions.

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Qian, Yili, Freeman Lan et Ophelia S. Venturelli. « Towards a deeper understanding of microbial communities : integrating experimental data with dynamic models ». Current Opinion in Microbiology 62 (août 2021) : 84–92. http://dx.doi.org/10.1016/j.mib.2021.05.003.

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Dean, Natalie, et Yang Yang. « Discussion of “Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data” ». Journal of the American Statistical Association 116, n^o 536 (2 octobre 2021) : 1587–90. http://dx.doi.org/10.1080/01621459.2021.1982722.

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Datta, Jyotishka, et Bhramar Mukherjee. « Discussion on “Regression Models for Understanding COVID-19 Epidemic Dynamics With Incomplete Data” ». Journal of the American Statistical Association 116, n^o 536 (2 octobre 2021) : 1583–86. http://dx.doi.org/10.1080/01621459.2021.1982721.

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Rosen, Seymour, et N. Heyman Samuel. « Difficulties in understanding human “acute tubular necrosis” : Limited data and flawed animal models ». Kidney International 60, n^o 4 (octobre 2001) : 1220–24. http://dx.doi.org/10.1046/j.1523-1755.2001.00930.x.

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Chandra, Satish, Jackie de Vries, John Field, Howard Hess, Manivannan Kalidasan, Komondoor V. Raghavan, Frans Nieuwerth, Ganesan Ramalingam et Justin Xue. « Technical forum : Using logical data models for understanding and transforming legacy business applications ». IBM Systems Journal 45, n^o 3 (2006) : 647–55. http://dx.doi.org/10.1147/sj.453.0647.

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Boler, Huseyin, Debakanta Mishra, Wenting Hou et Erol Tutumluer. « Understanding track substructure behavior : Field instrumentation data analysis and development of numerical models ». Transportation Geotechnics 17 (décembre 2018) : 109–21. http://dx.doi.org/10.1016/j.trgeo.2018.10.001.

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Zhang, Die, Yong Ge, Xilin Wu, Haiyan Liu, Wenbin Zhang et Shengjie Lai. « Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics ». ISPRS International Journal of Geo-Information 12, n^o 7 (3 juillet 2023) : 266. http://dx.doi.org/10.3390/ijgi12070266.

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Data-driven approaches predict infectious disease dynamics by considering various factors that influence severity and transmission rates. However, these factors may not fully capture the dynamic nature of disease transmission, limiting prediction accuracy and consistency. Our proposed data-driven approach integrates spatiotemporal human mobility patterns from detailed point-of-interest clustering and population flow data. These patterns inform the creation of mobility-informed risk indices, which serve as auxiliary factors in data-driven models for detecting outbreaks and predicting prevalence trends. We evaluated our approach using real-world COVID-19 outbreaks in Beijing and Guangzhou, China. Incorporating the risk indices, our models successfully identified 87% (95% Confidence Interval: 83–90%) of affected subdistricts in Beijing and Guangzhou. These findings highlight the effectiveness of our approach in identifying high-risk areas for targeted disease containment. Our approach was also tested with COVID-19 prevalence data in the United States, which showed that including the risk indices reduced the mean absolute error and improved the R-squared value for predicting weekly case increases at the county level. It demonstrates applicability for spatiotemporal forecasting of widespread diseases, contributing to routine transmission surveillance. By leveraging comprehensive mobility data, we provide valuable insights to optimize control strategies for emerging infectious diseases and facilitate proactive measures against long-standing diseases.

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Merrill, Evelyn, Håkan Sand, Barbara Zimmermann, Heather McPhee, Nathan Webb, Mark Hebblewhite, Petter Wabakken et Jacqueline L. Frair. « Building a mechanistic understanding of predation with GPS-based movement data ». Philosophical Transactions of the Royal Society B : Biological Sciences 365, n^o 1550 (27 juillet 2010) : 2279–88. http://dx.doi.org/10.1098/rstb.2010.0077.

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Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. We address current approaches to using global positioning system (GPS)-based movement data for quantifying key predation components of large carnivores. We review approaches to identify kill sites from GPS movement data as a means to estimate kill rates and address advantages of using GPS-based data over past approaches. Despite considerable progress, modelling the probability that a cluster of GPS points is a kill site is no substitute for field visits, but can guide our field efforts. Once kill sites are identified, time spent at a kill site (handling time) and time between kills (killing time) can be determined. We show how statistical models can be used to investigate the influence of factors such as animal characteristics (e.g. age, sex, group size) and landscape features on either handling time or killing efficiency. If we know the prey densities along paths to a kill, we can quantify the ‘attack success’ parameter in functional response models directly. Problems remain in incorporating the behavioural complexity derived from GPS movement paths into functional response models, particularly in multi-prey systems, but we believe that exploring the details of GPS movement data has put us on the right path.

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Hou, Weiying, et Chenshu Wu. « RFBoosT : Understanding and Boosting Deep WiFi Sensing via Physical Data Augmentation ». Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, n^o 2 (13 mai 2024) : 1–26. http://dx.doi.org/10.1145/3659620.

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Deep learning shows promising performance in wireless sensing. However, deep wireless sensing (DWS) heavily relies on large datasets. Unfortunately, building comprehensive datasets for DWS is difficult and costly, because wireless data depends on environmental factors and cannot be labeled offline. Despite recent advances in few-shot/cross-domain learning, DWS is still facing data scarcity issues. In this paper, we investigate a distinct perspective of radio data augmentation (RDA) for WiFi sensing and present a data-space solution. Our key insight is that wireless signals inherently exhibit data diversity, contributing more information to be extracted for DWS. We present RFBoost, a simple and effective RDA framework encompassing novel physical data augmentation techniques. We implement RFBoost as a plug-and-play module integrated with existing deep models and evaluate it on multiple datasets. Experimental results demonstrate that RFBoost achieves remarkable average accuracy improvements of 5.4% on existing models without additional data collection or model modifications, and the best-boosted performance outperforms 11 state-of-the-art baseline models without RDA. RFBoost pioneers the study of RDA, an important yet currently underexplored building block for DWS, which we expect to become a standard DWS component of WiFi sensing and beyond. RFBoost is released at https://github.com/aiot-lab/RFBoost.

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Jiang, Xufeng, Zelu Jia, Lefei Li et Tianhong Zhao. « Understanding Housing Prices Using Geographic Big Data : A Case Study in Shenzhen ». Sustainability 14, n^o 9 (28 avril 2022) : 5307. http://dx.doi.org/10.3390/su14095307.

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Understanding the spatial pattern of urban house prices and its association with the built environment is of great significance to housing policymaking and urban planning. However, many studies on the influencing factors of urban housing prices conduct qualitative analyses using statistical data and manual survey data. In addition, traditional housing price models are mostly linear models that cannot explain the distribution of housing prices in urban areas. In this paper, we propose using geographic big data and zonal nonlinear feature machine learning models to understand housing prices. First, the housing price influencing factor system is built based on the hedonic pricing model and geographic big data, and it includes commercial development, transportation, infrastructure, location, education, environment, and residents’ consumption level. Second, a spatial exploratory analysis framework for house price data was constructed using Moran’s I tools and geographic detectors. Finally, the XGBoost model is developed to assess the importance of the variables influencing housing prices, and the zonal nonlinear feature model is built to predict housing prices based on spatial exploration results. Taking Shenzhen as an example, this paper explored the distribution law of housing prices, analyzed the influencing factors of housing prices, and compared the different housing price models. The results show that the zonal nonlinear feature model has higher accuracy than the linear model and the global model.

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Wu, Likang, Zhaopeng Qiu, Zhi Zheng, Hengshu Zhu et Enhong Chen. « Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations ». Proceedings of the AAAI Conference on Artificial Intelligence 38, n^o 8 (24 mars 2024) : 9178–86. http://dx.doi.org/10.1609/aaai.v38i8.28769.

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Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in various domains. However, their potential for graph semantic mining in job recommendations remains largely unexplored. This paper focuses on unveiling the capability of large language models in understanding behavior graphs and leveraging this understanding to enhance recommendations in online recruitment, including promoting out-of-distribution (OOD) applications. We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs and uncover underlying patterns and relationships. Specifically, we propose a meta-path prompt constructor that aids LLM recommender in grasping the semantics of behavior graphs for the first time and design a corresponding path augmentation module to alleviate the prompt bias introduced by path-based sequence input. By facilitating this capability, our framework enables personalized and accurate job recommendations for individual users. We evaluate the effectiveness of our approach on comprehensive real-world datasets and demonstrate its ability to improve the relevance and quality of recommended results. This research not only sheds light on the untapped potential of large language models but also provides valuable insights for developing advanced recommendation systems in the recruitment market. The findings contribute to the growing field of natural language processing and offer practical implications for enhancing job search experiences.

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