1,721,007 research outputs found
Ji ben li zi wu li xue de yan jiu
No full textTsui, Ka Ming = 基本粒子物理學的研究 / 徐嘉明.Thesis M.Phil. Chinese University of Hong Kong 2015.Includes bibliographical references (leaves 150-157).Abstracts also in Chinese.Title from PDF title page (viewed on 03, January, 2017).Tsui, Ka Ming = Ji ben li zi wu li xue de yan jiu / Xu Jiaming
Matching in networks: fundamental limits and efficient algorithms
No full textIn recent years, there has been an explosion of online platforms and E-commerce, which generate massive amounts of network data. For example, a friend link on Facebook represents a connection between users, and a rating on Amazon is a connection between a customer and a product. These network data contain extensive information on customers' behaviors and preferences. However, deriving meaningful insights from the data can be challenging. On the one hand, networks are often extremely large and have millions of nodes and billions of edges. On the other hand, the network can be sparse, given that some nodes interact only with a few other nodes with a significant amount of noise from missing or faulty connections. Thus, extracting valuable information from such noisy and vast amounts of data requires highly efficient approaches that can process a large amount of data and detect tenuous statistical signatures. As such, my thesis has developed along the following two interrelated streams. The first stream aims at developing the fundamental limits and designing simple and scalable algorithms for learning complex networks. We base our study upon the decision-theoretic framework wherein an unknown structure underlies the network data. We aim to detect and recover this hidden structure based on partial or noisy observation. In particular, we focus on the problem of graph matching (network alignment), which refers to finding the bijection between the vertex sets of the two graphs that maximizes the total number of common edges. We have initiated a statistical analysis of detection and recovery problems by matching two randomly generated graphs with an underlying vertex correspondence and correlated edges. We characterize the sharp fundamental limits for both problems and develop new algorithms that are the first to achieve detection and recovery with an explicit constant correlation for both sparse and dense regimes, which settled important open problems in this field.The second stream focuses on improving network systems' decision-making efficiency under uncertainties and limited information. We study the dynamic matching problem in which a new agent enters the market randomly and waits to be matched, and the arrival rates for different types of agents are unknown. The goal is to maximize market efficiency and, at the same time, reduce the wait time for each participant in the system. This problem is inspired by the car-pooling platform, in which after they make a request, riders may have to wait on the platform for some time for potential matches with other riders traveling in the same direction based on their pick-up and drop-off locations. We develop hindsight-optimal primal-dual policies that achieve both constant regret and wait time for each type of agent on average at all times. </p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
End-to-End Federated Diffusion Generative Models for Tabular Data
No full textTabular data is widely used in various fields and applications, making the synthesis of such data an active area of research. One important aspect of this research is the development of methods for privacy-preserving data synthesis, which aims to generate synthetic data that retains statistical properties while protecting the privacy of individuals in the dataset. Recently, Diffusion Generative Models, such as Gaussian Diffusion Model, have significantly improved image synthesis, but their effectiveness in synthesizing tables is limited, because of using One-Hot encoding for representing categorical attributes with many categories. Furthermore, it needs the private data to be centrally collected for training, thus violating the privacy-preserving criteria. In this paper, we propose a new decentralized tabular synthesizing framework, which has three key features: (i) a decentralized Autoencoder comprised of an encoder and a decoder to map discrete features into the continuous space and back, (ii) a tabular diffusion model trained in a decentralized manner and (iii) incorporating differential privacy on central stochastic gradient training. We conduct extensive experimental studies that focus on sampling quality and diversity, using 9 tabular datasets and 4 state-of-the-art synthesizers. The results show that our method outperforms existing central methods by 10.7% and 31.4% in data quality and diversity on average, and 6.8% and 21.1% in data quality and diversity in scenarios facing non-IID data.Computer Scienc
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