Готові списки джерел за темами / Theoretical sampling framework

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Добірка наукової літератури з теми "Theoretical sampling framework"

Автор: Grafiati

Опубліковано: 10 січня 2023

Оновлено: 28 січня 2023

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Статті в журналах з теми "Theoretical sampling framework"

Qian, Hui, and Lun Yu. "Exponential Sampling: A Gibbs Phenomena Removal Model for Finite Rate of Innovation Sampling Framework." Applied Mechanics and Materials 130-134 (October 2011): 470–74. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.470.

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We propose an exponential approximating function as a sampling kernel with finite rate of innovation. The performance of reconstruct non-bandlimited signals from its low frequency components would inevitably induce Gibbs phenomenon. This paper establishes the theoretical model on relationship between sampling kernel filter and parametric reconstruction method of non-bandlimited signals, and designs a new window function exponential sampling kernel filter to removal Gibbs Influence. Simulation results show that, compared to Sinc sampling kernel filter, the reconstruction ability of exponential filter based finite rate sampling system is improved under the white Gaussian noise environment.

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Jaki, Thomas, and Martin J. Wolfsegger. "A Theoretical Framework for Estimation of AUCs in Complete and Incomplete Sampling Designs." Statistics in Biopharmaceutical Research 1, no. 2 (May 2009): 176–84. http://dx.doi.org/10.1198/sbr.2009.0025.

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Støa, Bente, Rune Halvorsen, Sabrina Mazzoni, and Vladimir I. Gusarov. "Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models." Sommerfeltia 38, no. 1 (October 1, 2018): 1–53. http://dx.doi.org/10.2478/som-2018-0001.

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Abstract This paper provides a theoretical understanding of sampling bias in presence-only data in the context of species distribution modelling. This understanding forms the basis for two integrated frameworks, one for detecting sampling bias of different kinds in presence-only data (the bias assessment framework) and one for assessing potential effects of sampling bias on species distribution models (the bias effects framework). We exemplify the use of these frameworks to museum data for nine insect species in Norway, for which the distribution along the two main bioclimatic gradients (related to oceanicity and temperatures) are modelled using the MaxEnt method. Models of different complexity (achieved by use of two different model selection procedures that represent spatial prediction or ecological response modelling purposes, respectively) were generated with different types of background data (uninformed and background-target-group [BTG]). The bias assessment framework made use of comparisons between observed and theoretical frequency-of-presence (FoP) curves, obtained separately for each combination of species and bioclimatic predictor, to identify potential sampling bias. The bias effects framework made use of comparisons between modelled response curves (predicted relative FoP curves) and the corresponding observed FoP curves for each combination of species and predictor. The extent to which the observed FoP curves deviated from the expected, smooth and unimodal theoretical FoP curve, varied considerably among the nine insect species. Among-curve differences were, in most cases, interpreted as indications of sampling bias. Using BTG-type background data in many cases introduced strong sampling bias. The predicted relative FoP curves from MaxEnt were, in general, similar to the corresponding observed FoP curves. This indicates that the main structure of the data-sets were adequately summarised by the MaxEnt models (with the options and settings used), in turn suggesting that shortcomings of input data such as sampling bias or omission of important predictors may overshadow the effect of modelling method on the predictive performance of distribution models. The examples indicate that the two proposed frameworks are useful for identification of sampling bias in presence-only data and for choosing settings for distribution modelling options such as the method for extraction of background data points and determining the appropriate level of model complexity.

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Abreu, Charlles R. A., and Fernando A. Escobedo. "A general framework for non-Boltzmann Monte Carlo sampling." Journal of Chemical Physics 124, no. 5 (February 7, 2006): 054116. http://dx.doi.org/10.1063/1.2165188.

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Swenson, David W. H., Jan-Hendrik Prinz, Frank Noe, John D. Chodera, and Peter G. Bolhuis. "OpenPathSampling: A Python Framework for Path Sampling Simulations. 1. Basics." Journal of Chemical Theory and Computation 15, no. 2 (October 18, 2018): 813–36. http://dx.doi.org/10.1021/acs.jctc.8b00626.

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Tu, Hung-Ming. "The Attractiveness of Adaptive Heritage Reuse: A Theoretical Framework." Sustainability 12, no. 6 (March 18, 2020): 2372. http://dx.doi.org/10.3390/su12062372.

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Adaptive heritage reuse is a useful method to bring new meaning into a culture, manage heritage sites, and promote tourism development. However, it is not always successful, and there is no theoretical framework to understand its attractiveness and value. This study aimed at developing such a theoretical framework based on the analysis of nine cases of adaptive heritage reuse in Taiwan. The probe question technique of qualitative interview was used to assess the attraction framework. A total of 90 respondents were interviewed based on constant comparative analysis with the sampling strategy of theoretical saturation. The results illustrate the heritage and activities of the reuse environments, including natural and regional environments. These environments produce recreational values, including self-growth, health benefits, and social benefits. As promoting activities is an important attraction for tourists in the heritage reuse environment, the natural environment can be used to plan and design heritage outdoor activities. Finally, the regional environment can be an important basis for assessing the feasibility of adaptive heritage reuse, including historical streets, surrounding tourist attractions, and high transportation accessibility. This theoretical framework can be used to achieve sustainable management of heritage sites.

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Xu, Jie, Si Zhang, Edward Huang, Chun-Hung Chen, Loo Hay Lee, and Nurcin Celik. "MO2TOS: Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling." Asia-Pacific Journal of Operational Research 33, no. 03 (June 2016): 1650017. http://dx.doi.org/10.1142/s0217595916500172.

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Simulation optimization can be used to solve many complex optimization problems in automation applications such as job scheduling and inventory control. We propose a new framework to perform efficient simulation optimization when simulation models with different fidelity levels are available. The framework consists of two novel methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT methodology uses the low-fidelity simulations to transform the original solution space into an ordinal space that encapsulates useful information from the low-fidelity model. The OS methodology efficiently uses high-fidelity simulations to sample the transformed space in search of the optimal solution. Through theoretical analysis and numerical experiments, we demonstrate the promising performance of the multi-fidelity optimization with ordinal transformation and optimal sampling (MO2TOS) framework.

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Qin, Guojun, and Jingfang Wang. "Random Sampling and Signal Bregman Reconstruction Based on Compressed Sensing." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 2 (November 1, 2016): 365. http://dx.doi.org/10.11591/ijeecs.v4.i2.pp365-372.

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Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted from as little as possible of the data by applying CS and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the structure and content of the information in the signal. In this paper, the signal is the sparse in the Fourier transform and random sparse sampling is advanced by programing random observation matrix for peak random base. The signal is successfully restored by the use of Bregman algorithm. The signal is described in the transform space, and a theoretical framework is established with a new signal descriptions and processing. By making the case to ensure that the information loss, signal is sampled at much lower than the Nyquist sampling theorem requiring rate,but also the signal is completely restored in high probability. The random sampling has following advantages: alias-free sampling frequency need not obey the Nyquist limit and higher frequency resolution.

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Zhao, Guilian, Dongmei Huang, Changxin Cai, and Peng Wu. "A Novel Sparse Framework for Angle and Frequency Estimation." Sensors 22, no. 22 (November 9, 2022): 8633. http://dx.doi.org/10.3390/s22228633.

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The topic of joint angle and frequency estimation (JAFE) has aroused extensive interests in the past decades. Current estimation algorithms mainly rely on the Nyquist sampling criterion. In order not to cause ambiguity for parameter estimation, the space–time intervals must be smaller than given thresholds, which results in complicated hardware costs and a huge computational burden. This paper aims to reduce the complexity for JAFE, and a novel sparsity-aware framework is proposed. Unlike the current uniform sampling architectures, the incoming narrow-band singles are sampled by a series of space–time coprime samplers. An improved rotational invariance estimator is introduced, which offers closed-form solutions for both angle and frequency estimation. The mathematical treatments indicate that our methodology is inherent in larger spatial/temporal aperture than the uniform sampling architectures; hence, it provides more accurate JAFE compared to alternative approaches relying on uniform sampling. Additionally, it attains nearly the same complexity as the current rotational invariance approach. Numerical results agree with the theoretical advantages of our methodology.

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Migliore, Marco. "Near Field Antenna Measurement Sampling Strategies: From Linear to Nonlinear Interpolation." Electronics 7, no. 10 (October 17, 2018): 257. http://dx.doi.org/10.3390/electronics7100257.

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The aim of this review paper is to discuss some of the advanced sampling techniques proposed in the last decade in the framework of planar near-field measurements, clarifying the theoretical basis of the different techniques, and showing the advantages in terms of number of measurements. Instead of discussing the details of the techniques, the attention is focused on their theoretical bases to give a gentle introduction to the techniques. For each sampling method, examples on a liner array are discussed to clarify the advantages and disadvantages of the method.

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Більше джерел

Дисертації з теми "Theoretical sampling framework"

Carroll, James Lamond. "A Bayesian Decision Theoretical Approach to Supervised Learning, Selective Sampling, and Empirical Function Optimization." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3413.pdf.

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O'Mahony, George Barry. "Barriers to Access: Identifying the Perceived Barriers to Participation in Tertiary Education Among Hospitality Employees." Thesis, 1997. https://vuir.vu.edu.au/40276/.

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Книги з теми "Theoretical sampling framework"

Goswami, Usha, Alan Power, Marie Lallier, and Andrea Facoetti, eds. Oscillatory “Temporal Sampling” and Developmental Dyslexia: Towards an Over-Arching Theoretical Framework. Frontiers Media SA, 2015. http://dx.doi.org/10.3389/978-2-88919-439-1.

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Частини книг з теми "Theoretical sampling framework"

Liu, Jun S., Rong Chen, and Tanya Logvinenko. "A Theoretical Framework for Sequential Importance Sampling with Resampling." In Sequential Monte Carlo Methods in Practice, 225–46. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3437-9_11.

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Maag Merki, Katharina, Urs Grob, Beat Rechsteiner, Andrea Wullschleger, Nathanael Schori, and Ariane Rickenbacher. "Regulation Activities of Teachers in Secondary Schools: Development of a Theoretical Framework and Exploratory Analyses in Four Secondary Schools Based on Time Sampling Data." In Accountability and Educational Improvement, 257–301. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69345-9_12.

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AbstractPrevious research has revealed that teachers’ regulation activities in schools are most relevant for sustainable school improvement. However, previous studies have severe methodological and theoretical shortcomings. This paper presents the results of a mixed-method case study at four lower secondary schools, in which we developed a framework for understanding regulation activities and processes in schools and analyzed teachers’ regulation activities by using time sampling data of teachers’ performance-related and situation-specific day-to-day activities over 3 weeks. Our results revealed that teachers engage in regulation activities only relatively seldom. Significant differences between teachers were found that are systematically related to the teachers’ specific roles in the school. Teachers rated their regulation activities as especially beneficial for teaching, student learning, and teachers’ learning but as less beneficial for team and school development. Small differences between schools were identified. Further, the results revealed significant correlations between teachers’ perceived benefit of the daily activities and teachers’ daily satisfaction. Based on the results, it can be concluded that the newly developed method appears to be suitable for recording teachers’ daily regulation activities in a (relatively) valid manner and, consequently, for use as a complement to existing instruments. Limitations are discussed, and the need for further research is described.

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Bijlsma, Hannah. "The Quality of Student Perception Questionnaires: A Systematic Review." In Student Feedback on Teaching in Schools, 47–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75150-0_4.

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AbstractStudent perceptions of teaching are promising for measuring the quality of teaching in primary and secondary education. However, generating valid and reliable measurements when using a student perception questionnaire (SPQ) is not self-evident. Many authors have pointed to issues that need to be taken into account when developing, selecting, and using an SPQ in order to generate valid and reliable scores. In this study, 22 SPQs that met the inclusion criteria used in the literature search were systematically evaluated by two reviewers. The reviewers were most positive about the theoretical basis of the SPQs and about the quality of the SPQ materials. According to their evaluation, most SPQs also had acceptable reliability and construct validity. However, norm information about the quality rating measures was often lacking and few sampling specifications were provided. Information about the features of the SPQs, if available, was also often not presented in an accessible way by the instrument developers (e.g., in a user manual), making it difficult for potential SPQ users to obtain an overview of the qualities of available SPQs in order to decide which SPQs best fit their own context and intended use. It is suggested to create an international database of SPQs and to develop a standardized evaluation framework to evaluate the SPQ qualities in order to provide potential users with the information they need to make a well-informed choice of an SPQ.

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Golia, Priyanka, Brendan Juba, and Kuldeep S. Meel. "A Scalable Shannon Entropy Estimator." In Computer Aided Verification, 363–84. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13185-1_18.

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AbstractQuantified information flow (QIF) has emerged as a rigorous approach to quantitatively measure confidentiality; the information-theoretic underpinning of QIF allows the end-users to link the computed quantities with the computational effort required on the part of the adversary to gain access to desired confidential information. In this work, we focus on the estimation of Shannon entropy for a given program $$\varPi $$ Π . As a first step, we focus on the case wherein a Boolean formula $$\varphi (X,Y)$$ φ ( X , Y ) captures the relationship between inputs X and output Y of $$\varPi $$ Π . Such formulas $$\varphi (X,Y)$$ φ ( X , Y ) have the property that for every valuation to X, there exists exactly one valuation to Y such that $$\varphi $$ φ is satisfied. The existing techniques require $$\mathcal {O}(2^m)$$ O ( 2 m ) model counting queries, where $$m = |Y|$$ m = | Y | .We propose the first efficient algorithmic technique, called $$\mathsf {EntropyEstimation}$$ EntropyEstimation to estimate the Shannon entropy of $$\varphi $$ φ with PAC-style guarantees, i.e., the computed estimate is guaranteed to lie within a $$(1\pm \varepsilon )$$ ( 1 ± ε ) -factor of the ground truth with confidence at least $$1-\delta $$ 1 - δ . Furthermore, $$\mathsf {EntropyEstimation}$$ EntropyEstimation makes only $$\mathcal {O}(\frac{min(m,n)}{\varepsilon ^2})$$ O ( m i n ( m , n ) ε 2 ) counting and sampling queries, where $$m = |Y|$$ m = | Y | , and $$n = |X|$$ n = | X | , thereby achieving a significant reduction in the number of model counting queries. We demonstrate the practical efficiency of our algorithmic framework via a detailed experimental evaluation. Our evaluation demonstrates that the proposed framework scales to the formulas beyond the reach of the previously known approaches.

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Franklin, M. I. "Introduction." In Sampling Politics, 1–30. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190855475.003.0001.

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This chapter presents the disciplinary debates and terms of reference informing this exploration of music making in which sampling practices play a fundamental role. It maps out the theoretical and methodological terrain that informs the “close listening” approach to analyzing these works in light of a burgeoning interest from across the spectrum of academic research and music journalism in the interrelationship between music and politics—however these two domains may be defined. Developing earlier work addressing debates about when, and how music and politics may mutually inform one another, this chapter presents the socio-musicological and interdisciplinary approach to examining how this relationship “sounds” in five case studies. The objective is to provide a more refined conceptual lexicon and analytical framework so that reader-listeners can listen to, and so “hear” the respective ‘musicking politics” at stake in each case, and do so in ways that go beyond focusing on lyrical content alone or requiring an advanced level of musical knowledge. This opening chapter and the conclusion (Chapter 7) work together in either direction.

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Hankin, David G., Michael S. Mohr, and Ken B. Newman. "Unequal probability sampling." In Sampling Theory, 140–72. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.003.0008.

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Equal probability selection is a special case of the general theory of probability sampling in which population units may be selected with unequal probabilities. Unequal selection probabilities are often based on auxiliary variable values which are measures of the sizes of population units, thus leading to the acronym (PPS)—“Probability Proportional to Size”. The Horvitz–Thompson (1953) theorem provides a unifying framework for design-based sampling theory. A sampling design specifies the sample space (set of all possible samples) and associated first and second order inclusion probabilities (probabilities that unit i, or units i and j, respectively, are included in a sample of size n selected from N according to some selection method). A valid probability sampling scheme must have all first order inclusion probabilities > 00 (i.e., every population unit must have a chance of being in the sample). Unbiased variance estimation is possible only for those schemes that guarantee that all second order inclusion probabilities exceed zero, thus providing theoretical justification for the absence of unbiased estimators of sampling variance in systematic sampling and other schemes for which some second order inclusion probabilities are zero. Numerous generalized Horvitz–Thompson (HT) estimators can be formed and all are consistent estimators because they are functions of consistent HT estimators. Unequal probability systematic sampling and Poisson sampling (the unequal probability counterpart to Bernoulli sampling for which sample size is a random variable) are also considered. Several R programs for selecting unequal probability samples and for calculating first and second order inclusion probabilities are posted at http://global.oup.com/uk/companion/hankin.

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Kumar, Madhan, Siddanagouda Policepatil, and Latha Lavanya Balakrishnan. "The Effects of Workplace Incivility on Employee Voice Behavior in EdTech Companies." In EdTech Economy and the Transformation of Education, 168–86. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8904-5.ch010.

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Drawing on stressor-strain outcome (SSO) and conservation of resource (COR) theoretical framework, this study examines employee irritation as a mediator and psychological detachment as the moderator in the relationship between workplace incivility on employee voice behavior. A cross-section design was used, with multi-source data through convenience sampling from 492 EdTech employees and their supervisors. The effect of moderator and mediator is examined by employing the SPSS process macro. Results revealed that the proposed hypothesis was supported. The findings of this study also open up new lines of research in the EdTech industry and provide implications for HRD professionals, practitioners, and scholars.

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Grubor, Aleksandar, and Nenad Djokic. "Consumer Shopping Orientations and Online Purchases of Rural Tourism Services." In Handbook of Research on Agricultural Policy, Rural Development, and Entrepreneurship in Contemporary Economies, 133–51. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9837-4.ch007.

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The main goal of the chapter is to find out which consumer shopping orientations can be brought in connection to purchases of the services of rural tourism in online channel. Besides theoretical framework concerning rural tourism services and their relation to sustainable development of rural areas, as well as factors of online purchases, the chapter presents the results of primary research. The research is part of wider research conducted concerning online shopping behavior in Serbia. A total of 300 respondents participated. Convenience sampling was implemented. The results confirm that online sale of rural tourism services can be explained when considering consumer shopping orientations and that these orientations can be connected to demographic characteristics of consumers. Finally, managerial implications are considered.

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Grubor, Aleksandar, and Nenad Djokic. "Consumer Shopping Orientations and Online Purchases of Rural Tourism Services." In Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business, 1203–20. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8957-1.ch062.

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Merriam, Sharan B. "Qualitative Research." In Advances in Educational Marketing, Administration, and Leadership, 346–60. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4144-2.ch019.

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This chapter takes the reader on a step-by-step journey through the process of conducting a qualitative research study using research conducted with traditional healers (THs) in Malaysia and how they diagnose and treat cancer. Upwards of 80% of Malaysians consult traditional healers before seeing a medical doctor, resulting in late-stage diagnoses and thus higher mortality rates. However, prior to this research, little was known about the role of healers and their willingness to work with, rather than outside, the Western medical system. Within this context, the theoretical framework, the specific research problem, and the research questions were identified. Next, the authors discuss purposive sampling and data collection strategies which included interviews, documents, and observations. They then present a data analysis exhibit showing how they captured specific data from the interviews to address their research questions. Finally, the authors discuss writing and publishing the results of their research.

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Тези доповідей конференцій з теми "Theoretical sampling framework"

Kim, Yong Se, Yeon Koo Hong, Sun Ran Kim, and Jin Hui Kim. "Context-Specific Experience Sampling for User Emotion Research." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48682.

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Though benefits of Experience Sampling (ES) for experience design and research are apparent, the method has not been widely used in the field. As for the reasons, the following are prominent: 1) methodological issues as conventional ES obtains contextual experience information from the participants’ description of the context), and 2) a lack of theoretical framework enabling researchers to carry systematical analysis and extraction of meaningful experiences. In order to deal with these issues, the researchers have created an adapted ES model, named ‘Context-Specific Experience Sampling’, by which integration of a rigorous data collection and analysis processes is made possible. The model provides explanations of how to gather context-specific user experience information and extract key themes and attributes from the data pool. This approach, manifesting divergent-to-convergent features, is described as ‘experience pooling, sorting, and extracting’ which fall under the concept of ‘experience processing’. This paper details the structure and procedure of the model illustrating it with examples from a small scale lighting ambiance study of fashion stores.

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Wu, Chuhan, Fangzhao Wu, and Yongfeng Huang. "Rethinking InfoNCE: How Many Negative Samples Do You Need?" In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/348.

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InfoNCE is a widely used contrastive training loss. It aims to estimate the mutual information between a pair of variables by discriminating between each positive pair and its associated K negative pairs. It is proved that when the sample labels are clean, the lower bound of mutual information estimation is tighter when more negative samples are incorporated, which usually yields better model performance. However, in practice the labels often contain noise, and incorporating too many noisy negative samples into model training may be suboptimal. In this paper, we study how many negative samples are optimal for InfoNCE in different scenarios via a semi-quantitative theoretical framework. More specifically, we first propose a probabilistic model to analyze the influence of the negative sampling ratio K on training sample informativeness. Then, we design a training effectiveness function to measure the overall influence of training samples based on their informativeness. We estimate the optimal negative sampling ratio using the K value that maximizes the training effectiveness function. Based on our framework, we further propose an adaptive negative sampling method that can dynamically adjust the negative sampling ratio to improve InfoNCE-based model training. Extensive experiments in three different tasks show our framework can accurately predict the optimal negative sampling ratio, and various models can benefit from our adaptive negative sampling method.

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Krause, Oswin, Asja Fischer, and Christian Igel. "Algorithms for Estimating the Partition Function of Restricted Boltzmann Machines (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/704.

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Estimating the normalization constants (partition functions) of energy-based probabilistic models (Markov random fields) with a high accuracy is required for measuring performance, monitoring the training progress of adaptive models, and conducting likelihood ratio tests. We devised a unifying theoretical framework for algorithms for estimating the partition function, including Annealed Importance Sampling (AIS) and Bennett's Acceptance Ratio method (BAR). The unification reveals conceptual similarities of and differences between different approaches and suggests new algorithms. The framework is based on a generalized form of Crooks' equality, which links the expectation over a distribution of samples generated by a transition operator to the expectation over the distribution induced by the reversed operator. Different ways of sampling, such as parallel tempering and path sampling, are covered by the framework. We performed experiments in which we estimated the partition function of restricted Boltzmann machines (RBMs) and Ising models. We found that BAR using parallel tempering worked well with a small number of bridging distributions, while path sampling based AIS performed best with many bridging distributions. The normalization constant is measured w.r.t.~a reference distribution, and the choice of this distribution turned out to be very important in our experiments. Overall, BAR gave the best empirical results, outperforming AIS.

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Bouneffouf, Djallel, Raphael Feraud, Sohini Upadhyay, Irina Rish, and Yasaman Khazaeni. "Toward Optimal Solution for the Context-Attentive Bandit Problem." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/481.

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In various recommender system applications, from medical diagnosis to dialog systems, due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration; however, the agent has a freedom to choose which variables to observe. In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, We derive a novel algorithm, called Context-Attentive Thompson Sampling (CATS), which builds upon the Linear Thompson Sampling approach, adapting it to Context-Attentive Bandit setting. We provide a theoretical regret analysis and an extensive empirical evaluation demonstrating advantages of the proposed approach over several baseline methods on a variety of real-life datasets.

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Zhang, Xuemiao, Zhouxing Tan, Xiaoning Zhang, Yang Cao, and Rui Yan. "Adaptively Multi-Objective Adversarial Training for Dialogue Generation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/397.

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Naive neural dialogue generation models tend to produce repetitive and dull utterances. The promising adversarial models train the generator against a well-designed discriminator to push it to improve towards the expected direction. However, assessing dialogues requires consideration of many aspects of linguistics, which are difficult to be fully covered by a single discriminator. To address it, we reframe the dialogue generation task as a multi-objective optimization problem and propose a novel adversarial dialogue generation framework with multiple discriminators that excel in different objectives for multiple linguistic aspects, called AMPGAN, whose feasibility is proved by theoretical derivations. Moreover, we design an adaptively adjusted sampling distribution to balance the discriminators and promote the overall improvement of the generator by continuing to focus on these objectives that the generator is not performing well relatively. Experimental results on two real-world datasets show a significant improvement over the baselines.

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Huang, Sheng-Jun, Chen-Chen Zong, Kun-Peng Ning, and Hai-Bo Ye. "Asynchronous Active Learning with Distributed Label Querying." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/354.

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Active learning tries to learn an effective model with lowest labeling cost. Most existing active learning methods work in a synchronous way, which implies that the label querying can be performed only after the model updating in each iteration. While training models is usually time-consuming, it may lead to serious latency between two queries, especially in the crowdsourcing environments where there are many online annotators working simultaneously. This will significantly decrease the labeling efficiency and strongly limit the application of active learning in real tasks. To overcome this challenge, we propose a multi-server multi-worker framework for asynchronous active learning in the distributed environment. By maintaining two shared pools of candidate queries and labeled data respectively, the servers, the workers and the annotators efficiently corporate with each other without synchronization. Moreover, diverse sampling strategies from distributed workers are incorporated to select the most useful instances for model improving. Both theoretical analysis and experimental study validate the effectiveness of the proposed approach.

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Goba-Medne, Līva. "Learning Experience that Transforms Teachers’ Professional Activity: The Covid-19 Pandemic." In 80th International Scientific Conference of the University of Latvia. University of Latvia Press, 2022. http://dx.doi.org/10.22364/htqe.2022.34.

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Transforming of teaching and learning paradigms in education has been a long-standing object for debate. Various educational reform movements have formed along with divergent views on what quality education is. The COVID-19 pandemic has created a world-wide disruption not only to the continuity of education, but to the existing teaching strategies and learning modes as well. Teachers had to reorient their professional activity towards remote and hybrid teaching within a short period of time. This creates a crisis-like learning situation that requires not only the acquisition of practical skills, but it demands the transformation of the teaching activity itself. Learning associated with such transformations deserves closer examination. The aim of this article is to discuss the theoretical and the practical underpinnings of a study which aims at conceptualizing learning experiences of teachers that have accompanied the transformations of their professional activity during the COVID-19 pandemic situation. In–depth literature review was performed using both key word search and reference chaining to discover key authors, key texts, and key ideas. Further on central concepts were analysed and methodology was developed for researching the learning experiences teachers have undergone during the COVID-19 pandemic that have transformed their professional activity. Interpretivist subject-centred approach in line with social constructivism is applied. The article concludes that the central concepts – learning experiences of teachers, transformative learning, professional activity of teachers – serve to build a logical and compatible research framework. Narrative interview along with documentary method of interpretation and theoretical sampling forms coherent and validated methodology.

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Jin, Wei-Liang. "A New Approach for Dynamic Reliability Analysis of Offshore Platforms." In ASME 2003 22nd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2003. http://dx.doi.org/10.1115/omae2003-37046.

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The theoretical framework of a new dynamic reliability analysis approach for offshore jacket platforms is constructed in this paper. In this approach, the structural dynamic reliability analysis integrates the structural dynamic response theory with the random sampling technique on stochastic variables of the platform. Offshore jacket platform is assumed as a shear type one and is controlled by its horizontal resistance bearing capacity. The failure criterion of offshore platform dynamic reliability includes two aspects: strength failure criterion and deformation failure criterion. The strength failure criterion is based on the double-damage criterion of ultimate dynamic analysis, while the deformation failure criterion depends on the requirement of the offshore oil production. The dynamic reliability formula of offshore platforms is gained on the basis of the first crossing theory. In this paper, uncertainties of offshore jacket platform dynamic reliability subjected to wave hydrodynamic and seismic action are studied respectively. The probabilistic distributions and its statistical characteristics of wave hydrodynamic and seismic action are given on the base of limit bearing state of the platform. The dynamic reliability analysis of an offshore platform W11-4C, located at the gulf of Weizhou, South China Sea, is carried out. Some valuable conclusions are summarized.

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Betiku, Adedola, and Bassey Okon Bassey. "Exploring the Barriers to Implementation of Carbon Capture, Utilisation and Storage in Nigeria." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22387-ms.

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Abstract The global economy has increased CCUS technology development programmes to attain its commercial deployment, which is expected to be beneficial for developing countries such as Nigeria. This paper aims to examine the barriers to CCUS implementation in Nigeria by investigating the differences between global CCUS and Nigerian status, evaluating the perspectives of industry and government practitioners on the economic barriers to CCUS implementation, and identifying policy and industry strategies to deepen the adoption of CCUS. Study participants were selected using a purposive sampling technique to explore the opinions of personnel working in three oil-related agencies: Nigerian National Petroleum Corporation, Ministry of Petroleum Resources and Nigerian Liquefied Natural Gas. Information collected from existing literature and related reports on CCUS were critically analysed, whereas data from semi-structured interviews were generated by audio-recording of participants’ responses. These responses were transcribed from audio recordings for each participant and quality controlled by ensuring that transcripts matched the respective responses. Transcripts were analysed using thematic analysis, exploring the research theme using both theory and practice. The theoretical framework utilised PESTEL and SWOT analyses to evaluate the macro environment and the internal and external environment of CCUS implementation in Nigeria. PESTEL analysis showed that CCUS implementation in Nigeria is driven by various regulatory and policy frameworks, lack of adequate capital, public acceptance and infrastructure. Similarly, the SWOT analysis showed that Nigeria has enough coal reserves that could serve as a potential for CCUS implementation. However, Nigeria’s weaknesses include lack of expertise in CCUS technology, inadequate capital for CCUS investment and policy summersaults by successive governments. Nigeria should thus consider the introduction of subsidies to mitigate various barriers and challenges that hinder CCUS implementation, e.g., low tax rate for enterprises involved in CCUS implementation. There is also urgent need to improve funding of CCUS implementation through foreign direct investment or by the equity market. Furthermore, the importance for an enhanced technology to deepen the adoption of CCUS in Nigeria can not be overemphasized as the world moves towards decarbonisation and Net Zero.

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Zhao, Weibing, Xu Yan, Jiantao Gao, Ruimao Zhang, Jiayan Zhang, Zhen Li, Song Wu, and Shuguang Cui. "PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/186.

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Point Cloud Sampling and Recovery (PCSR) is critical for massive real-time point cloud collection and processing since raw data usually requires large storage and computation. This paper addresses a fundamental problem in PCSR: How to downsample the dense point cloud with arbitrary scales while preserving the local topology of discarded points in a case-agnostic manner (i.e., without additional storage for point relationships)? We propose a novel Locally Invertible Embedding (PointLIE) framework to unify the point cloud sampling and upsampling into one single framework through bi-directional learning. Specifically, PointLIE decouples the local geometric relationships between discarded points from the sampled points by progressively encoding the neighboring offsets to a latent variable. Once the latent variable is forced to obey a pre-defined distribution in the forward sampling path, the recovery can be achieved effectively through inverse operations. Taking the recover-pleasing sampled points and a latent embedding randomly drawn from the specified distribution as inputs, PointLIE can theoretically guarantee the fidelity of reconstruction and outperform state-of-the-arts quantitatively and qualitatively.

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Звіти організацій з теми "Theoretical sampling framework"

Russo, David, Daniel M. Tartakovsky, and Shlomo P. Neuman. Development of Predictive Tools for Contaminant Transport through Variably-Saturated Heterogeneous Composite Porous Formations. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7592658.bard.

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The vadose (unsaturated) zone forms a major hydrologic link between the ground surface and underlying aquifers. To understand properly its role in protecting groundwater from near surface sources of contamination, one must be able to analyze quantitatively water flow and contaminant transport in variably saturated subsurface environments that are highly heterogeneous, often consisting of multiple geologic units and/or high and/or low permeability inclusions. The specific objectives of this research were: (i) to develop efficient and accurate tools for probabilistic delineation of dominant geologic features comprising the vadose zone; (ii) to develop a complementary set of data analysis tools for discerning the fractal properties of hydraulic and transport parameters of highly heterogeneous vadose zone; (iii) to develop and test the associated computational methods for probabilistic analysis of flow and transport in highly heterogeneous subsurface environments; and (iv) to apply the computational framework to design an “optimal” observation network for monitoring and forecasting the fate and migration of contaminant plumes originating from agricultural activities. During the course of the project, we modified the third objective to include additional computational method, based on the notion that the heterogeneous formation can be considered as a mixture of populations of differing spatial structures. Regarding uncertainly analysis, going beyond approaches based on mean and variance of system states, we succeeded to develop probability density function (PDF) solutions enabling one to evaluate probabilities of rare events, required for probabilistic risk assessment. In addition, we developed reduced complexity models for the probabilistic forecasting of infiltration rates in heterogeneous soils during surface runoff and/or flooding events Regarding flow and transport in variably saturated, spatially heterogeneous formations associated with fine- and coarse-textured embedded soils (FTES- and CTES-formations, respectively).We succeeded to develop first-order and numerical frameworks for flow and transport in three-dimensional (3-D), variably saturated, bimodal, heterogeneous formations, with single and dual porosity, respectively. Regarding the sampling problem defined as, how many sampling points are needed, and where to locate them spatially in the horizontal x₂x₃ plane of the field. Based on our computational framework, we succeeded to develop and demonstrate a methdology that might improve considerably our ability to describe quntitaively the response of complicated 3-D flow systems. The results of the project are of theoretical and practical importance; they provided a rigorous framework to modeling water flow and solute transport in a realistic, highly heterogeneous, composite flow system with uncertain properties under-specified by data. Specifically, they: (i) enhanced fundamental understanding of the basic mechanisms of field-scale flow and transport in near-surface geological formations under realistic flow scenarios, (ii) provided a means to assess the ability of existing flow and transport models to handle realistic flow conditions, and (iii) provided a means to assess quantitatively the threats posed to groundwater by contamination from agricultural sources.

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