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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
A Feasibility Investigation of Modular Portable “Chelson Shelters” Micro-Homes to Alleviate Housing Deficiencies: A Case Study in Mithi, Tharpakar, Pakistan
Full text linkMany people in Mithi, Tharparkar do not have proper housing, face an unhygienic water supply, and have no sanitation facilities. These factors contribute to disease, suffering and the inability to rise above their destitute existence. The idea for building portable houses for the people of Mithi is presented to provide them with better living conditions and where they can feel a sense of security, ownership and sanitation. Research on existing building systems and materials showed that the most feasible structure for the desert environment is modular panels attached to a core unit that contains all the basic plumbing and electrical fixtures. The unit can be expanded based on family needs. Discussion with government officials showed that these could be used for the immediate needs of the people who have been suffering more acutely the last several years due to a drought. They could also be a permanent solution to the housing crisis if the Chelson Shelter communities worked well for ten years. The infrastructure in the Tharparker Desert is inadequate to support typical housing. These shelters have low environmental impact, use little water and electricity and would be a good solution to make a community of people that can support each other and provide security
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Early evolution of coal nitrogen in opposed flow combustion configurations.
No full textA laminar opposed flow, pulverized coal combustion configuration was used to explore the early evolution of light gaseous nitrogenous and hydrocarbon species into the bulk gas phase. Two coals of different ranks were considered. Effects of pyrolysis environment, particle size and heating rates were investigated. Concentration profiles of HCN, NH₃, NO, CH₄, C₂H₂, C₂H₄ and C₂H₆ were measured, under both oxidizing and reducing environments, for three particle sizes, and at high heating rates provided by the hot flue gases of a CO/O₂/Ar flame. Net rates of formation into the bulk gas phase were calculated from the experimental data after correcting for diffusion and convection effects, and were then related to particle time-temperature histories. Experimental data show that HCN precedes NH₃ and NO for both coals. It is the first light gaseous product of coal nitrogen evolution entering into the bulk gas phase. For low rank coals, either only a small amount of tar nitrogen is released or its subsequent oxidation to light gaseous products is slow. For high rank coals secondary reactions of tars are rapid and lead to substantial levels of nitrogenous species. Nature of nitrogenous species evolving into the bulk gas phase was found to be independent of particle size. Lower heating rates favor increased yields of ammonia. Evolution of hydrocarbon species from high rank coals occurs via low molecular weight species, whereas low rank coals yield high molecular weight species. Evolution of hydrocarbon species was found to be independent of particle size and heating rates. Evolution of hydrogen occurs during late stages of devolatilization indicating that it is a product of secondary pyrolysis reactions. A simple kinetic model is proposed to relate rates of formation of nitrogenous species to coal devolatilization kinetics. The latter are similar for three experiments, with fine particles, involving two coals and can be described by a single rate constant given by 63.8 exp (-5220/RT). Bituminous coal (fines), under oxidizing conditions, shows substantially higher rates, possibly due to energy feedback mechanisms in the vicinity of the particles. Literature values, which originated from solid phase measurements, underpredict the quantities of total XN entering the post flame zone by substantial amounts. Our value, which was derived from gas phase species measurements, yields a better prediction of total nitrogenous species entering the post flame zone, and can be incorporated in engineering models aiming at optimizing of pollutant emissions.p. 106, p. 107, p. 108 are missing from paper original and microfilm version.This item was digitized from a paper original and/or a microfilm copy. If you need higher-resolution images for any content in this item, please contact us at [email protected] file replaced with corrected file April 2023
Data and image domain deep learning for computational imaging
Full text linkDeep learning has overwhelmingly impacted post-acquisition image-processing tasks, however, there is increasing interest in more tightly coupled computational imaging approaches, where models, computation, and physical sensing are intertwined. This dissertation focuses on how to leverage the expressive power of deep learning in image reconstruction. We use deep learning in both the sensor data domain and the image domain to develop new fast and efficient algorithms to achieve superior quality imagery.
Metal artifacts are ubiquitous in both security and medical applications. They can greatly limit subsequent object delineation and information extraction from the images, restricting their diagnostic value. This problem is particularly acute in the security domain, where there is great heterogeneity in the objects that can appear in a scene, highly accurate decisions must be made quickly, and the processing time is highly constrained. Motivated primarily by security applications, we present a new deep-learning-based MAR approach that tackles the problem in the sensor data domain. We treat the observed data corresponding to dense, metal objects as missing data and train an adversarial deep network to complete the missing data directly in the projection domain. The subsequent complete projection data is then used with an efficient conventional image reconstruction algorithm to reconstruct an image intended to be free of artifacts.
Conventional image reconstruction algorithms assume that high-quality data is present on a dense and regular grid. Using conventional methods when these requirements are not met produces images filled with artifacts that are difficult to interpret. In this context, we develop data-domain deep learning methods that attempt to enhance the observed data to better meet the assumptions underlying the fast conventional analytical reconstruction methods. By focusing learning in the data domain in this way and coupling the result with existing conventional reconstruction methods, high-quality imaging can be achieved in a fast and efficient manner. We demonstrate results on four different problems: i) low-dose CT, ii) sparse-view CT, iii) limited-angle CT, and iv) accelerated MRI.
Image domain prior models have been shown to improve the quality of reconstructed images, especially when data are limited. A novel principled approach is presented allowing the unified integration of both data and image domain priors for improved image reconstruction. The consensus equilibrium framework is extended to integrate physical sensor models, data models, and image models. In order to achieve this integration, the conventional image variables used in consensus equilibrium are augmented with variables representing data domain quantities. The overall result produces combined estimates of both the data and the reconstructed image that is consistent with the physical models and prior models being utilized. The prior models used in both image and data domains in this work are created using deep neural networks. The superior quality allowed by incorporating both data and image domain prior models is demonstrated for two applications: limited-angle CT and accelerated MRI.
A major question that arises in the use of neural networks and in particular deep networks is their stability. That is, if the examples seen in the application environment differ from the training environment will the performance be robust. We perform an empirical stability analysis of data and image domain deep learning methods developed for limited-angle CT reconstruction. We consider three types of perturbations to test stability: adversarially optimized, random, and structural perturbations. Our empirical analysis reveals that the data-domain learning approach proposed in this dissertation is less susceptible to perturbations as compared to the image-domain post-processing approach. This is a very encouraging result and strongly supports the main argument of this dissertation that there is value in using data-domain learning and it should be a part of our computational imaging toolkit
Inheritance of resistance to green leafhopper, Nephotettix virescens (Distant) in some rice varieties
No full textAppropriate 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
Dendritic spine shape analysis based on two-photon microscopy images
Full text linkNeuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. Previously, volumes of dendritic spines have been considered as a primary parameter to study spine morphology and gain insight into structure-function coupling. However, this reductionist approach fails to incorporate the broad spine structure repertoire. First step towards integrating the rich spine morphology information into functional coupling is to classify spine shapes into main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is currently performed manually, which is a time-intensive task and prone to subjectivity. Availability of automated spine shape analysis tools can accelerate this process and help neuroscientists understand underlying structure and function relationship. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. This thesis focuses on morphological, shape, and appearance features based methods to perform dendritic spine shape analysis using both clustering and classification approaches. We apply manifold learning methods for dendritic spine classification and observe that ISOMAP implicitly computes prominent features suitable for classification purposes. We also apply linear representation based approach for spine classification and conclude that sparse representation provides slightly better classification performance. We propose 2D and 3D morphological features based approach for spine shape analysis and demonstrate the advantage of 3D morphological features. We also use a deep learning based approach for spine classification and show that mid-level features extracted from Convolutional Neural Networks (CNNs) perform as well as hand-crafted features. We propose a kernel density estimation (KDE) based framework for dendritic spine classification. We evaluate our proposed approaches by comparing labels assigned by a neuroscience expert. Our KDE based framework also enables neuroscientists to analyze separability of spine shape classes in the likelihood ratio space, which leads to further insights about the nature of the spine shape analysis problem. Furthermore, we also propose a methodology for unsupervised learning and clustering of spine shapes. In particular, we use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). The objective of clustering in this context is two-fold: confirm the hypothesis of some distinct shape classes and discover new natural groups. We observe that although there are many spines which easily fit into the definition of standard shape types (confirming the hypothesis), there are also a significant number of others which do not comply with standard shape types and demonstrate intermediate properties
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
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