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Thematische Bibliographien / Relationship inference / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Relationship inference“

Autor: Grafiati

Veröffentlicht am 4. Juni 2025

Zuletzt aktualisiert am 1. August 2025

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1

Nkiinebari Patrick PhD, Nwinyokpugi, and Ezeukwu, Kate Chukwunonso. "Stakeholders Management Indicators: The Business Sustainability Inference." International Journal of Multidisciplinary Research and Analysis 05, no. 10 (2022): 2789–803. http://dx.doi.org/10.47191/ijmra/v5-i10-30.

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The study investigated the relationship between stakeholder relationship management and business sustainability indigenous oil and gas producing companies in Rivers State. The study population comprised of the ten (10) indigenous oil and gas producing companies operating in Rivers State. The research selected variated number of managers from each of the firms under study making it a total sample of sixty two (66) respondents. Data were generated from the respondents by the use of a close-ended structured questionnaire. Pearson’s product moment correlation, partial correlation and multiple regr

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Koscik, Timothy R., and Daniel Tranel. "The Human Ventromedial Prefrontal Cortex Is Critical for Transitive Inference." Journal of Cognitive Neuroscience 24, no. 5 (2012): 1191–204. http://dx.doi.org/10.1162/jocn_a_00203.

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We hypothesized that the ventromedial pFC (vmPFC) is critical for making transitive inferences (e.g., the logical operation that if A > B and B > C, then A > C). To test this, participants with focal vmPFC damage, brain-damaged comparison participants, and neurologically normal participants completed a transitive inference task consisting an ordered set of arbitrary patterns. Participants first learned through trial-and-error the relationships of the patterns (e.g., Pattern A > Pattern B, Pattern B > Pattern C). After initial learning, participants were presented with novel pair

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Novick, Laura R., Kefyn M. Catley, and Daniel J. Funk. "Inference Is Bliss: Using Evolutionary Relationship to Guide Categorical Inferences." Cognitive Science 35, no. 4 (2011): 712–43. http://dx.doi.org/10.1111/j.1551-6709.2010.01162.x.

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Duff, Katharine E., and John P. Smol. "The relationship of chrysophycean stomatocysts to environmental variables in freshwater lakes in British Columbia." Canadian Journal of Botany 73, no. 7 (1995): 1097–111. http://dx.doi.org/10.1139/b95-119.

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The relationships between the distributions of 82 chrysophycean cyst morphotypes and measured environmental variables in freshwater lakes in British Columbia were examined using ordination and regression statistics. After removal of unusual samples, 60 lakes were included in the analyses. Indirect and direct gradient analysis explained 23.2 and 14.0% of the variance in the cyst distribution data, and 31.4 and 53.7% of the variance in the cyst–environment relationship, respectively. Watershed area, Secchi depth and [Mg] were identified as the variables with the greatest contributions to the fir

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Melgarejo, Teófilo Félix Valentín, Pablo Lenin La Madrid Vivar, Clodoaldo Ramos Pando, Pablo Lolo Valentín Melgarejo, and Agustín Arturo Aguirre Adauto. "Inference and reading comprehension in university students." Nurture 18, no. 4 (2024): 785–94. http://dx.doi.org/10.55951/nurture.v18i4.846.

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Purpose: The objective of this research was to determine the relationship between inference and reading comprehension. We sought to verify the relationship between inductive and deductive inferences in the comprehensive reading of Daniel Alcides Carrión National University Peru students majoring in communication and literature. Design/Methodology/Approach: The correlational-explanatory research design was used since the correlation of the study variables was sought through scientific and specifically analytical, deductive and interpretive methodology on a population of 104 and the probabilisti

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Tarlowski, Andrzej. "Naming Patterns and Inductive Inference: The Case of Birds." Journal of Cognition and Culture 11, no. 1-2 (2011): 189–216. http://dx.doi.org/10.1163/156853711x568743.

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AbstractAlthough past research demonstrated that online presentation of labels plays a role in inductive inference few studies have shown that naming practices affect stable category representations that enter into inductive judgments. In this study we provide evidence for a relationship between naming and inductive inference by examining Polish and Spanish speakers’ inferences within the taxonomic class Aves. Birds in Polish are named with one label, ptak, while Spanish uses two labels, ave and pájaro. Size is the feature that determines whether Spanish speakers label a bird as ave or pájaro.

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Shambharkar, Saroj, K. Vaishali, Rachna Somkunwar, Yogeshri Choudhari, and Jyotsna Gawai. "Biological Network, Gene Regulatory Network Inference Using Causal Inference Approach." Revue d'Intelligence Artificielle 36, no. 1 (2022): 139–45. http://dx.doi.org/10.18280/ria.360116.

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In system biology inference from gene regulatory network (GRN) is a challenging task. There exist different computational techniques to analyze the causal relationships between the pair of genes and to understand the significance of causal relationship in gene regulatory network. The DREAM4 insilico network structure and insilico gene expression time series dataset of DREAM challenge dataset is examined. This gene expression dataset of insilico of size 10 is analyzed for inferring causal relationships of the GRN inference. The analysis of dataset showing the gene expression data values are var

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Moeschler, Jacques. "What makes inferences reliable? The unpredictable relationship between pragmatic inference and truth." Journal of Pragmatics 218 (December 2023): 153–64. http://dx.doi.org/10.1016/j.pragma.2023.10.010.

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Inohara, Keisuke, Ryoko Honma, Takayuki Goto, Takashi Kusumi, and Akira Utsumi. "The relationship between reading literary novels and predictive inference generation." Scientific Study of Literature 4, no. 1 (2014): 46–67. http://dx.doi.org/10.1075/ssol.4.1.03ino.

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This study examined the relationship between reading literary novels and generating predictive inferences by analyzing a corpus of Japanese novels. Latent semantic analysis (LSA) was used to capture the statistical structure of the corpus. Then, the authors asked 74 Japanese college students to generate predictive inferences (e.g., “The newspaper burned”) in response to Japanese event sentences (e.g., “A newspaper fell into a bonfire”) and obtained more than 5,000 predicted events. The analysis showed a significant relationship between LSA similarity between the event sentences and the predict

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KURATSUNE, Masanori. "ON THE INFERENCE OF CAUSAL RELATIONSHIP." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 14, no. 1 (1986): 79–84. http://dx.doi.org/10.2333/jbhmk.14.79.

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Kunbin Qu, Nan Lin, Yanmei Lu, and D. G. Payan. "Multidimensional data integration and relationship inference." IEEE Intelligent Systems 17, no. 2 (2002): 21–27. http://dx.doi.org/10.1109/5254.999216.

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Kaur, Navreet, Mariam M. Bouzga, Guro Dørum, and Thore Egeland. "Relationship inference based on DNA mixtures." International Journal of Legal Medicine 130, no. 2 (2015): 323–29. http://dx.doi.org/10.1007/s00414-015-1276-1.

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Qu, Kunbin, Nan Lin, Yanmei Lu, and Donald Payan. "Multidimensional data integration and relationship inference." IEEE Intelligent Systems 17, no. 2 (2002): 21–27. http://dx.doi.org/10.1109/mis.2002.4629857.

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Budziak, Jeffrey, and Daniel Lempert. "Assessing Threats to Inference with Simultaneous Sensitivity Analysis: The Case of US Supreme Court Oral Arguments." Political Science Research and Methods 6, no. 1 (2015): 33–56. http://dx.doi.org/10.1017/psrm.2015.74.

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Political scientists relying on observational data face substantial challenges in drawing causal inferences. A particularly problematic threat to inference is the unobserved confounder. As a means to assess this threat, we introduce simultaneous sensitivity analysis to the political science literature. As an application, we consider the potentially confounded relationship between Supreme Court justice voting and oral argument quality. We demonstrate that this relationship is sensitive to the presence of a confounder, to a degree that threatens inference, and explore the confounder both theoret

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Din, Muhammad, and Mamuna Ghani. "Evaluating University Students’ Inference Making Ability: A Study at Bachelor Level in Pakistan." International Journal of English Linguistics 9, no. 5 (2019): 351. http://dx.doi.org/10.5539/ijel.v9n5p351.

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Inference means the process of deriving a conclusion from a set of premises, including a conclusion that is probably in relation to the premises. This study has aimed to evaluate university students&rsquo; inference making ability. To explore this aim, the present study has set five research objectives which include to understand university students&rsquo; attitude towards critical thinking subset of making inferences, find out the relationship between university students&rsquo; attitude towards making inferences and their ability in making inferences, know whether critical thinkin

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Nwinyokpugi, Nkiinebari Patrick PhD. "Stakeholders Management Indicators: The Business Sustainability Inference." Stakeholders Management Indicators: The Business Sustainability Inference 05, no. 10 (2022): 2789–803. https://doi.org/10.5281/zenodo.7215291.

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The study investigated the relationship between stakeholder relationship management and business sustainability indigenous oil and gas producing companies in Rivers State. The study population comprised of the ten (10) indigenous oil and gas producing companies operating in Rivers State. The research selected variated number of managers from each of the firms under study making it a total sample of sixty two (66) respondents. Data were generated from the respondents by the use of a close-ended structured questionnaire. Pearson’s product moment correlation, partial correlation and multipl

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Harun, Rashed, Eric Yang, Nastya Kassir, Wenhui Zhang, and James Lu. "Machine Learning for Exposure-Response Analysis: Methodological Considerations and Confirmation of Their Importance via Computational Experimentations." Pharmaceutics 15, no. 5 (2023): 1381. http://dx.doi.org/10.3390/pharmaceutics15051381.

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Exposure-response (E-R) is a key aspect of pharmacometrics analysis that supports drug dose selection. Currently, there is a lack of understanding of the technical considerations necessary for drawing unbiased estimates from data. Due to recent advances in machine learning (ML) explainability methods, ML has garnered significant interest for causal inference. To this end, we used simulated datasets with known E-R “ground truth” to generate a set of good practices for the development of ML models required to avoid introducing biases when performing causal inference. These practices include the

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Liu, Yiming, Hongtao Shan, Feng Nie, Gaoyu Zhang, and George Xianzhi Yuan. "Document-Level Relation Extraction with Local Relation and Global Inference." Information 14, no. 7 (2023): 365. http://dx.doi.org/10.3390/info14070365.

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The current popular approach to the extraction of document-level relations is mainly based on either a graph structure or serialization model method for the inference, but the graph structure method makes the model complicated, while the serialization model method decreases the extraction accuracy as the text length increases. To address such problems, the goal of this paper is to develop a new approach for document-level relationship extraction by applying a new idea through the consideration of so-called “Local Relationship and Global Inference” (in short, LRGI), which means that we first en

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Shabaz, Mohammad, and Mukesh Soni. "Integrating Relationship Path and Entity Neighbourhood Information for Knowledge Graph Intelligence of Social Things." Chinese Journal of Information Fusion 2, no. 1 (2025): 27–37. https://doi.org/10.62762/cjif.2025.197460.

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In the evolving framework of the Intelligence of Social Things (IoST), which amalgamates social networks and IoT ecosystems, knowledge graphs are essential for facilitating networked systems to efficiently process and leverage intricate relational data. Knowledge graphs offer essential technical assistance for various artificial intelligence applications, such as e-commerce, intelligent navigation, healthcare, and social media. Nonetheless, current knowledge graphs frequently lack completeness, harboring a considerable quantity of implicit knowledge that remains to be revealed. Consequently, t

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(River) Huang, Ho-Chuan, and Shu-Chin Lin. "A flexible nonlinear inference to Okun's relationship." Applied Economics Letters 13, no. 5 (2006): 325–31. http://dx.doi.org/10.1080/13504850500398625.

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MAEDA, Masahiro. "Text Coherence Necessary for the Promotion of Japanese EFL Learner’s Predictive Inference Generation." SALTeL Journal (Southeast Asia Language Teaching and Learning) 6, no. 1 (2023): 34–40. http://dx.doi.org/10.35307/saltel.v6i1.105.

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Reading is a very complex process consisting of lower- and higher-level processing. Inference generation is necessary to achieve higher-level processing. It is challenging for Japanese EFL readers because they must use many cognitive resources in lower-level processing, such as word recognition and syntax analysis. This study investigated the relationship between Japanese EFL learners' predictive inference generation as higher-level processing and text coherence from the encoding perspective rather than activation. We used a cued recall task whose rates would be evidence of the reader's encodi

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Li, Jie, Fanzi Zeng, Zhu Xiao, Zhirun Zheng, Hongbo Jiang, and Zhetao Li. "Social Relationship Inference Over Private Vehicle Mobility Data." IEEE Transactions on Vehicular Technology 70, no. 6 (2021): 5221–33. http://dx.doi.org/10.1109/tvt.2021.3060787.

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Epstein, Michael P., William L. Duren, and Michael Boehnke. "Improved Inference of Relationship for Pairs of Individuals." American Journal of Human Genetics 67, no. 5 (2000): 1219–31. http://dx.doi.org/10.1016/s0002-9297(07)62952-8.

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Dørum, Guro, Navreet Kaur, and Mario Gysi. "Pedigree-based relationship inference from complex DNA mixtures." International Journal of Legal Medicine 131, no. 3 (2017): 629–41. http://dx.doi.org/10.1007/s00414-016-1526-x.

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Manichaikul, Ani, Josyf C. Mychaleckyj, Stephen S. Rich, Kathy Daly, Michèle Sale, and Wei-Min Chen. "Robust relationship inference in genome-wide association studies." Bioinformatics 26, no. 22 (2010): 2867–73. http://dx.doi.org/10.1093/bioinformatics/btq559.

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Nikitina, Irina Nikolaevna, Natalya Valerevna Panina, and Ekaterina Valerevna Kopshukova. "Peculiarities of conveying logical inferences when translating an English simple sentence into Russian." Philology. Issues of Theory and Practice 17, no. 4 (2024): 1079–85. http://dx.doi.org/10.30853/phil20240157.

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The aim of the study is to identify regular correspondences in the ways of representing logical inferences when translating a simple English sentence into Russian. The scientific novelty of the study lies in the fact that a comparative analysis of the linguistic forms of the original and the translation is carried out for the first time at two levels of the text (logical and linguistic) and the ways of translating such a complex form of thought as a logical inference from English into Russian are identified. As a result, it was found that in most cases, the form of thought (inference) is prese

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Naseri, Ardalan, Junjie Shi, Xihong Lin, Shaojie Zhang, and Degui Zhi. "RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID." PLOS Genetics 17, no. 1 (2021): e1009315. http://dx.doi.org/10.1371/journal.pgen.1009315.

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Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ

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Richards, Laura J., and Jon T. Schnute. "Use of a General Dose–Response Model for Rockfish Fecundity–Length Relationships." Canadian Journal of Fisheries and Aquatic Sciences 47, no. 6 (1990): 1148–56. http://dx.doi.org/10.1139/f90-134.

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In this paper we describe a general method for determining the relationship between fecundity and another fish attribute, such as size or age. Our methods include linear and logarithmic regression models as special cases and are applicable to a wide range of situations. The model we propose is based on the univariate form of the Schnute–Jensen dose–response model. However, we extend the Schnute–Jensen analysis by describing exact inference regions obtained from likelihood contours, to which we assign nominal probability levels. We also provide a method for obtaining an inference band for the p

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Watanabe, Kandai, Georgios Fainekos, Bardh Hoxha, et al. "Timed Partial Order Inference Algorithm." Proceedings of the International Conference on Automated Planning and Scheduling 33, no. 1 (2023): 639–47. http://dx.doi.org/10.1609/icaps.v33i1.27246.

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In this work, we propose the model of timed partial orders (TPOs) for specifying workflow schedules, especially for modeling manufacturing processes. TPOs integrate partial orders over events in a workflow, specifying ``happens-before'' relations, with timing constraints specified using guards and resets on clocks -- an idea borrowed from timed-automata specifications. TPOs naturally allow us to capture event ordering, along with a restricted but useful class of timing relationships. Next, we consider the problem of mining TPO schedules from workflow logs, which include events along with their

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Thestrup Waade, Peter, Christoffer Lundbak Olesen, Jonathan Ehrenreich Laursen, et al. "As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference." Entropy 27, no. 2 (2025): 143. https://doi.org/10.3390/e27020143.

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Active inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour and self-maintenance. Crucially, a collective of active inference agents can, if they maintain a group-level Markov blanket, constitute a larger group-level active inference agent with a generative model of its own. This potential for computational scale-free structures speaks to the application of active inference to self-organizing systems across spatiotemporal scales, from cells to human collectives. Due to the difficulty of reconstructing the

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Chen, Yile, Xiucheng Li, Gao Cong, et al. "Points-of-interest relationship inference with spatial-enriched graph neural networks." Proceedings of the VLDB Endowment 15, no. 3 (2021): 504–12. http://dx.doi.org/10.14778/3494124.3494134.

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As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers. Most of the existing methods for relationship inference are not targeted at POI, thus failing to capture unique spatial characteristics that have huge effects on POI relationships. In this work we propose PRIM to tackle POI relationship inference for multiple relation types. PRIM features four novel components, including a weighted relational graph neural network, category taxo

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Hall, Wayne. "A Simplified Logic of Causal Inference." Australian & New Zealand Journal of Psychiatry 21, no. 4 (1987): 507–13. http://dx.doi.org/10.3109/00048678709158918.

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This paper provides a simplified method for evaluating the evidence in favour of a causal claim. It analyses the evidence bearing upon such a claim in terms of two questions: Do the putative cause and effect covary, and can alternative non-causal explanations of the relationship be ruled out? The different research designs for assessing covariation are outlined, as are the ways in which these designs permit a researcher to decide between alternative explanations of the relationship.

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Wang, Siruo, Tyler H. McCormick, and Jeffrey T. Leek. "Methods for correcting inference based on outcomes predicted by machine learning." Proceedings of the National Academy of Sciences 117, no. 48 (2020): 30266–75. http://dx.doi.org/10.1073/pnas.2001238117.

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Many modern problems in medicine and public health leverage machine-learning methods to predict outcomes based on observable covariates. In a wide array of settings, predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between observed and predicted outcomes. We call inference with predicted outcomes postprediction inference. In this paper, we develop methods for correcting statistical inference using outcomes predicted with arbitrarily complicated machine-learning models including random forests and deep neural nets. Rather than trying t

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Qin, Guangming, Lexue Song, Yanwei Yu, et al. "Graph Structure Learning on User Mobility Data for Social Relationship Inference." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4578–86. http://dx.doi.org/10.1609/aaai.v37i4.25580.

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With the prevalence of smart mobile devices and location-based services, uncovering social relationships from human mobility data is of great value in real-world spatio-temporal applications ranging from friend recommendation, advertisement targeting to transportation scheduling. While a handful of sophisticated graph embedding techniques are developed for social relationship inference, they are significantly limited to the sparse and noisy nature of user mobility data, as they all ignore the essential problem of the existence of a large amount of noisy data unrelated to social activities in s

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Haldar, Nur Al Hasan, Mark Reynolds, Quanxi Shao, Cecile Paris, Jianxin Li, and Yunliang Chen. "Activity location inference of users based on social relationship." World Wide Web 24, no. 4 (2021): 1165–83. http://dx.doi.org/10.1007/s11280-021-00899-y.

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Regenwetter, Michel, and Ilia Tsetlin. "Approval voting and positional voting methods: Inference, relationship, examples." Social Choice and Welfare 22, no. 3 (2004): 539–66. http://dx.doi.org/10.1007/s00355-003-0232-z.

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Zhao, Hongyu, and Feng Liang. "On Relationship Inference Using Gamete Identity by Descent Data." Journal of Computational Biology 8, no. 2 (2001): 191–200. http://dx.doi.org/10.1089/106652701300312940.

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Zhou, Tong, and Ya-Li Wang. "Causal relationship inference for a large-scale cellular network." Bioinformatics 26, no. 16 (2010): 2020–28. http://dx.doi.org/10.1093/bioinformatics/btq325.

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Lin, Sheng-Hsuan, and Mohammad Arfan Ikram. "On the relationship of machine learning with causal inference." European Journal of Epidemiology 35, no. 2 (2019): 183–85. http://dx.doi.org/10.1007/s10654-019-00564-9.

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Li, Saihan, Zhijie Hu, and Rong Cao. "Natural language inference by deep learning method." MATEC Web of Conferences 355 (2022): 03028. http://dx.doi.org/10.1051/matecconf/202235503028.

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Natural Language inference refers to the problem of determining the relationships between a premise and a hypothesis, it is an emerging area of natural language processing. The paper uses deep learning methods to complete natural language inference task. The dataset includes 3GPP dataset and SNLI dataset. Gensim library is used to get the word embeddings, there are 2 methods which are word2vec and doc2vec to map the sentence to array. 2 deep learning models DNNClassifier and Attention are implemented separately to classify the relationship between the proposals from the telecommunication area

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Rohrer, Julia M., and Kou Murayama. "These Are Not the Effects You Are Looking for: Causality and the Within-/Between-Persons Distinction in Longitudinal Data Analysis." Advances in Methods and Practices in Psychological Science 6, no. 1 (2023): 251524592211408. http://dx.doi.org/10.1177/25152459221140842.

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In psychological science, researchers often pay particular attention to the distinction between within- and between-persons relationships in longitudinal data analysis. Here, we aim to clarify the relationship between the within- and between-persons distinction and causal inference and show that the distinction is informative but does not play a decisive role in causal inference. Our main points are threefold. First, within-persons data are not necessary for causal inference; for example, between-persons experiments can inform about (average) causal effects. Second, within-persons data are not

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Qadir, Sherko Hamaamin, and Ehsan Sabir M. Rashid. "Inference of the Meanings Idiom." Halabja University Journal 7, no. 4 (2022): 1–20. http://dx.doi.org/10.32410/huj-10429.

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Semantics as a part of the sciences related to meaning and the interpretation of semantic features and signs has an own important. Regarding the meaning of fixed-expressions and idioms as one of these expressions, this study is entitled (Inference of the meanings of the Idiom) tries to interpret idioms as a set of fixed-expressions and carry Lexical and non-Lexical meanings Present other views,Thus, after presenting the definition and characteristics of idioms, giving the necessary examples and analysis of examples related to meanings analysis and the meaning of letters and non-letters, idioms

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Kimmet, Faith, Samantha Pedersen, Victoria Cardenas, et al. "Metacognition and Causal Inference in Audiovisual Speech." Multisensory Research 36, no. 3 (2023): 289–311. http://dx.doi.org/10.1163/22134808-bja10094.

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Abstract In multisensory environments, our brains perform causal inference to estimate which sources produce specific sensory signals. Decades of research have revealed the dynamics which underlie this process of causal inference for multisensory (audiovisual) signals, including how temporal, spatial, and semantic relationships between stimuli influence the brain’s decision about whether to integrate or segregate. However, presently, very little is known about the relationship between metacognition and multisensory integration, and the characteristics of perceptual confidence for audiovisual s

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Hatch, Oliver J., Hannah A. Agbaroji, Estefania Valencia, and David J. Hardy. "A-121 Drinking Behavior and Impulsivity in College Students." Archives of Clinical Neuropsychology 37, no. 6 (2022): 1273. http://dx.doi.org/10.1093/arclin/acac060.121.

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Abstract Objective: Individuals with chronic alcohol abuse have been shown to be more impulsive. Our objective in the present study was to assess drinking behavior in healthy college students and examine potential relationships between these drinking habits and impulsive behavior. Method: College students (n = 22) completed a survey about drinking behaviors, the Barratt Impulsiveness Scale (assessing various subscales of impulsivity), and the Color-Word Inference Test portion of the Delis Kaplan Executive Function System (DKEFS). The Color-Word Inference Test is a variation of the Stroop Test,

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LaFlair, Geoffrey T., and Shelley Staples. "Using corpus linguistics to examine the extrapolation inference in the validity argument for a high-stakes speaking assessment." Language Testing 34, no. 4 (2017): 451–75. http://dx.doi.org/10.1177/0265532217713951.

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Investigations of the validity of a number of high-stakes language assessments are conducted using an argument-based approach, which requires evidence for inferences that are critical to score interpretation (Chapelle, Enright, & Jamieson, 2008b; Kane, 2013). The current study investigates the extrapolation inference for a high-stakes test of spoken English, the Michigan English Language Assessment Battery (MELAB) speaking task. This inference requires evidence that supports the inferential step from observations of what test takers can do on an assessment to what they can do in the target

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Miklin, Nikolai, Mariami Gachechiladze, George Moreno, and Rafael Chaves. "Causal inference with imperfect instrumental variables." Journal of Causal Inference 10, no. 1 (2022): 45–63. http://dx.doi.org/10.1515/jci-2021-0065.

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Abstract Instrumental variables allow for quantification of cause and effect relationships even in the absence of interventions. To achieve this, a number of causal assumptions must be met, the most important of which is the independence assumption, which states that the instrument and any confounding factor must be independent. However, if this independence condition is not met, can we still work with imperfect instrumental variables? Imperfect instruments can manifest themselves by violations of the instrumental inequalities that constrain the set of correlations in the scenario. In this art

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Appel, Benjamin J. "Causal Inference, International Law, And Maritime Disputes." AJIL Unbound 115 (2021): 389–93. http://dx.doi.org/10.1017/aju.2021.62.

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Sara Mitchell and Andrew Owsiak's examination of the impact of UN Convention on the Law of the Sea (UNCLOS) and Article 287 declarations on the peaceful resolution of maritime disputes significantly advances the literature on the relationship between international law/international courts and maritime issues. To their credit, the authors employ a wide range of empirical tests in the article to provide readers with confidence in the empirical results. Nonetheless, there are some important limitations in their approach. Drawing on insights from the causal inference literature, I argue that Mitch

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Fagin, Ronald, Joseph Y. Halpern, and Moshe Y. Vardi. "What is an inference rule?" Journal of Symbolic Logic 57, no. 3 (1992): 1018–45. http://dx.doi.org/10.2307/2275447.

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AbstractWhat is an inference rule? This question does not have a unique answer. One usually finds two distinct standard answers in the literature; validity inference (σ ⊦vφ for every substitution τ, the validity of τ[σ] entails the validity of τ[φ]), and truth inference (σ⊦l φ if for every substitution τ, the truth of τ[σ] entails the truth of τ[φ]). In this paper we introduce a general semantic framework that allows us to investigate the notion of inference more carefully. Validity inference and truth inference are in some sense the extremal points in our framework. We investigate the relatio

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Gershman, Samuel J. "Dopamine, Inference, and Uncertainty." Neural Computation 29, no. 12 (2017): 3311–26. http://dx.doi.org/10.1162/neco_a_01023.

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The hypothesis that the phasic dopamine response reports a reward prediction error has become deeply entrenched. However, dopamine neurons exhibit several notable deviations from this hypothesis. A coherent explanation for these deviations can be obtained by analyzing the dopamine response in terms of Bayesian reinforcement learning. The key idea is that prediction errors are modulated by probabilistic beliefs about the relationship between cues and outcomes, updated through Bayesian inference. This account can explain dopamine responses to inferred value in sensory preconditioning, the effect

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Smith, Maverick E., John P. Hutson, Mi’Kayla Newell, et al. "Bridging a Gap in Coherence: The Coordination of Comprehension Processes When Viewing Visual Narratives." Vision 8, no. 3 (2024): 50. http://dx.doi.org/10.3390/vision8030050.

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Scene Perception and Event Comprehension Theory (SPECT) posits that understanding picture stories depends upon a coordination of two processes: (1) integrating new information into the current event model that is coherent with it (i.e., mapping) and (2) segmenting experiences into distinct event models (i.e., shifting). In two experiments, we investigated competing hypotheses regarding how viewers coordinate the mapping process of bridging inference generation and the shifting process of event segmentation by manipulating the presence/absence of Bridging Action pictures (i.e., creating coheren

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