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"influence analysis"
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Training data influence analysis and estimation: a survey
Good models require good training data. For overparameterized deep models, the causal relationship between training data and model predictions is increasingly opaque and poorly understood. Influence analysis partially demystifies training’s underlying interactions by quantifying the amount each training instance alters the final model. Measuring the training data’s influence exactly can be provably hard in the worst case; this has led to the development and use of influence estimators, which only approximate the true influence. This paper provides the first comprehensive survey of training data influence analysis and estimation. We begin by formalizing the various, and in places orthogonal, definitions of training data influence. We then organize state-of-the-art influence analysis methods into a taxonomy; we describe each of these methods in detail and compare their underlying assumptions, asymptotic complexities, and overall strengths and weaknesses. Finally, we propose future research directions to make influence analysis more useful in practice as well as more theoretically and empirically sound.
Journal Article
How to find opinion leader on the online social network?
Online social networks (OSNs) provide a platform for individuals to share information, exchange ideas, and build social connections beyond in-person interactions. For a specific topic or community, opinion leaders are individuals who have a significant influence on others’ opinions. Detecting opinion leaders and modeling influence dynamics is crucial as they play a vital role in shaping public opinion and driving conversations. Existing research have extensively explored various graph-based and psychology-based methods for detecting opinion leaders, but there is a lack of cross-disciplinary consensus between definitions and methods. For example, node centrality in graph theory does not necessarily align with the opinion leader concepts in social psychology. This review paper aims to address this multi-disciplinary research area by introducing and connecting the diverse methodologies for identifying influential nodes. The key novelty is to review connections and cross-compare different multi-disciplinary approaches that have origins in: social theory, graph theory, compressed sensing theory, and control theory. Our first contribution is to develop cross-disciplinary discussion on how they tell a different tale of networked influence. Our second contribution is to propose trans-disciplinary research method on embedding socio-physical influence models into graph signal analysis. We showcase inter- and trans-disciplinary methods through a Twitter case study to compare their performance and elucidate the research progression with relation to psychology theory. We hope the comparative analysis can inspire further research in this cross-disciplinary area.
Journal Article
Spatial Distributions and Intrinsic Influence Analysis of Cr, Ni, Cu, Zn, As, Cd and Pb in Sediments from the Wuliangsuhai Wetland, China
The spatial distributions of Cr, Ni, Cu, Zn, As, Cd and Pb (potentially toxic elements, PTEs) in sediments and intrinsic influence factors from the Wuliangsuhai wetland of the Hetao Irrigation District, China were studied in this work. The results showed that excluding Zn, the total contents of other PTEs were higher than the background values, of which As (39.26 mg·kg−1) and Cd (0.44 mg·kg−1) were six-fold and seven-fold higher, respectively. Especially, the high levels of Cd (70.17%), Pb (66.53%), and Zn (57.20%) in the non-residual fraction showed high bioavailability and mobility. It indicated that PTEs can enter the food chain more easily and produce much toxicity. Based on Igeo, ICF, and MRI, the contamination of As was the most serious in the middle areas (MDP) of the wetland, and its risk was up to moderately strong. Cd and Pb posed moderate and considerate risk, respectively. Furthermore, 29.50% and 55.54% risk contribution ratio of As and Cd, respectively, showed that they were the dominant contaminants. In addition, the positive correlation between sand, OM, and total contents and chemical fractions of PTEs by using PCM, RDA, and DHCA indicated that physicochemical properties could significantly influence the spatial distributions of PTEs. The work was useful for assessing the level of pollution in the study area and acquiring information for future and possible monitoring and remediation activities.
Journal Article
Gold and freedom : the political economy of Reconstruction
\"This book argues that Northern disputes over public debt, greenbacks, and tariffs, as well as national economic consequences of the Civil War, undermined Reconstruction as much as Southern race relations and constitutional issues\"--Provided by publisher.
Book
The status, challenges, and trends: an interpretation of technology roadmap of intelligent and connected vehicles in China (2020)
PurposeThe rapid development of Intelligent and Connected Vehicles (ICVs) has boomed a new round of global technological and industrial revolution in recent decades. The Technology Roadmap of Intelligent and Connected Vehicles (2020) comprehensively analyzes the technical architecture, research status and future trends of ICVs. The methodology that supports the roadmap should get studied.Design/methodology/approachThis paper interprets the roadmap from the aspects of strategic significance, technical content and characteristics of the roadmap, and evaluates the impact of the roadmap on researchers, industries and international strategies.FindingsThe technical architecture of ICVs as the “three rows and two columns” structure is studied, the methodology that supported the roadmap is explained with a case study and the influence of key technologies with proposed development routes is analyzed.Originality/valueThis paper could help researchers understand both thoughts and methodologies behind the technology roadmap of ICVs.
Journal Article
Wagner, Schumann, and the lessons of Beethoven's Ninth
\"Reynolds shows that the stylistic advances made by Richard Wagner and Robert Schumann in 1845-46 stemmed from a deepened understanding of Beethoven's techniques and strategies in the Ninth Symphony, particularly the use of counterpoint involving contrary motion. The trail of influences that Reynolds explores extends back to the music of Bach and ahead to Tristan and Isolde, as well as to Brahms's First Symphony.\"--Provided by publisher.
BOOK
A Novel Influence Analysis-Based University Major Similarity Study
In the field of education, investigating the relationships between different majors in universities is an important topic in current educational research. The application of social networks from informatics provides new opportunities and potentials for the field of education. Due to the complexity of social interactions, the social network connections surrounding individuals exert a significant influence on their daily decision-making processes. This paper aims to introduce the social network and influence analysis theories from informatics into the field of education, regarding major as a variable, and comparing and analyzing the influence relationships between majors. An empirical study was conducted, involving the collection of questionnaire data on graduates’ evaluations of various aspects of their university experiences across different majors. The evolution of this model follows the DeGroot opinion dynamics with the inclusion of stubborn nodes. By defining leader majors and general majors based on the data and modeling the questionnaire data as the outcome of a discrete random process, an influence matrix is ultimately generated through the opinion dynamic model. Through this modeling approach, we revealed the underlying influence relationships between different disciplines (majors). These findings provide schools with insights to adjust the directions of discipline cultivation, and offer new perspectives and methods for the study of majors in higher education.
Journal Article