1,721,005 research outputs found
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
Artificial Language Evolution on a Dynamical Interaction Network
No full text"This dissertation studies the impact of a dynamical interaction network on the distributed learning of a common language. We derive a new algorithm for generating realistic complex networks, called Noisy Preferential Attachment (NPA). This is a modification of preferential attachment that unifies it with the quasispecies model of molecular evolution. The growing network can now be seen as a process in which the links in the network are undergoing selection, replication, and mutation. We also demonstrate that by varying the mutation rate over time, we can reproduce features of growing networks in the real world. We then model a population of language learning agents on an interaction topology evolving according to NPA and demonstrate that under certain conditions they can converge very rapidly. However, we also note that they always converge to a maximally simple language. This leads us to introduce a method of relating language to task based on an analogy between the agents' hypothesis space and an information channel. We introduce a new ""language game"" which we call the classification game. We show that the population, through playing the classification game, converges to a representation which is simple, but not too simple, by balancing the pressures for learnability and functionality. We demonstrate that the population can avoid overfitting through this process. The languages that emerge can be either holistic or compositional. We then introduce temporal tasks and show that the same setup, using recurrent neural networks and form-meaning association matrices, can generate languages with strict symbol ordering, which is a rudimentary form of syntax. Finally, we bring together language and topology evolution and show that when the classification game is played on a topology evolving according to NPA, very rapid convergence can be achieved at the expense of a small increase in complexity of the solution. We also compare the convergence rates of several other topologies and show that NPA results in the fastest convergence. Regular and small world topologies show very slow convergence, due to the formation of communities which are locally converged but at odds with other communities."Made available in DSpace on 2015-09-25T20:20:29Z (GMT). No. of bitstreams: 2
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Previous issue date: 2007Embargo set by: Seth Robbins for item 83074
Lift date: Forever
Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDsRestricted to the U of I community idenfinitely during batch ingest of legacy ETDsU of I Only114 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007
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
Human Behavior Modeling and Calibration in Epidemic Simulations
No full textHuman behavior plays an important role in infectious disease epidemics. The choice of preventive actions taken by individuals can completely change the epidemic outcome. Computational epidemiologists usually employ large-scale agent-based simulations of human populations to study disease outbreaks and assess intervention strategies. Such simulations rarely take into account the decision-making process of human beings when it comes to preventive behaviors. Absence of realistic agent behavior can undermine the reliability of insights generated by such simulations and might make them ill-suited for informing public health policies. In this thesis, we address this problem by developing a methodology to create and calibrate an agent decision-making model for a large multi-agent simulation, in a data driven way. Our method optimizes a cost vector associated with the various behaviors to match the behavior distributions observed in a detailed survey of human behaviors during influenza outbreaks. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations.Master of ScienceIn the real world, individuals can decide to adopt certain behaviors that reduce their chances of contracting a disease. For example, using hand sanitizers can reduce an individual‘s chances of getting infected by influenza. These behavioral decisions, when taken by many individuals in the population, can completely change the course of the disease. Such behavioral decision-making is generally not considered during in-silico simulations of infectious diseases. In this thesis, we address this problem by developing a methodology to create and calibrate a decision making model that can be used by agents (i.e., synthetic representations of humans in simulations) in a data driven way. Our method also finds a cost associated with such behaviors and matches the distribution of behavior observed in the real world with that observed in a survey. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations
Agreement, Information and Time in Multiagent Systems
No full textThis dissertation studies multiagent agreement problems -- problems in which a population of agents must agree on some quantity or behavior in a distributed manner. Agreement problems are central in many areas, from the study of magnetism (Ising model), to understanding the diffusion of innovations (such as the diffusion of hybrid corn planting in Illinois), to modeling linguistic change.
The thesis of this dissertation is that the ability for agents to optimally allocate resources towards 1) gaining information from which to infer the agreeing population's global agreement state (``information gathering'') and 2) effectively using that information to make convergence decisions that move towards agreement (``information use''), are the fundamental factors that explain the performance of a distributed agreement-seeking collective, and that variations on these processes capture all prevalent styles of agreement problems.
In this dissertation we develop a taxonomic framework that organizes a wide range of agreement problems according to constraints on information gathering and information use. We explore two specific instances of agreement problems in more depth; the first modulates information gathering by constraining the ability of agents to communicate; the second modulates information use by constraining the ability of agents to change states.
An understanding of these two components will allow the application of insights from fields such as statistical physics, distributed algorithms, and multiagent systems to bear on language -- and in turn carry insights from linguistic agreement to these fields. Note, however, that the purpose of this dissertation is not to model natural phenomena, but rather to explore, through abstract models, some of the fundamental processes that underlie natural phenomena.
Our first contribution is to develop the \emph{Distributed Optimal Agreement} framework -- a taxonomic framework through which we can formally identify potential constraints on the two processes of information gathering and use.
Our second contribution is to develop an understanding of the \emph{Fundamental Agreement Tradeoff}, which is a relation between the effort an agent expends to gather information, the accuracy of the information gathered, and the amount of time it takes for a population to reach agreement.
We develop the \gssm\ process as a way to explore the fundamental agreement tradeoff by modulating the amount of effort an agent can expend, which in turn affects the accuracy of information gathered. We show, surprisingly, that a population can reach agreement quickly even with a minimal expenditure of effort. This result has impact for any setting in which communication is a resource intensive procedure (e.g., energy constrained sensor networks). We provide extensive numerical simulations of the \gssm\ process in a variety of settings. In addition, we we analytically show that the \gssm\ process reaches agreement under a mean-field assumption.
Our third contribution is to study agreement in complex spaces with boundedly rational agents where there are significant restrictions on communication. We develop the \emph{Distributed Constraint Agreement} problem (which itself is a type of agreement problem that can be captured by the DOA framework) in order to explore the impact of bounded rationality and communication on agreement in complex spaces.
As an example scenario we abstractly model the linguistic phenomenon of the Great English Vowel Shift (GEVS) -- a shift in the pronunciation of certain vowels that took place between 1450 and 1750. We define a simple algorithm and through extensive simulation show that a vowel shift could have occurred if a new population of linguistic users, with slightly different pronunciations, entered the linguistic community. These results lend support to the ``migration'' hypothesis for the GEVS -- that due to casualties from the Black Death the linguistic composition of upper class England changed to incorporate individuals with different pronunciations.
Together, these three contributions move us closer to forming a general theory of agreement.Item withdrawn by Mark Zulauf ([email protected]) on 2009-10-23T18:42:17Z
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University of Illinois Theses & Dissertations (ID: 1)
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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
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