1,721,005 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
Neural Network Verification as Piecewise Linear Optimization: Formulations for the Composition of Staircase Functions
No full textWe present a technique for neural network verification using mixed-integer programming (MIP) formulations.
We derive a \emph{strong formulation} for each neuron in a network using piecewise linear activation functions.
Additionally, as in general, these formulations may require an exponential number of inequalities, we also derive a separation procedure that runs in super-linear time in the input dimension.
We first introduce and develop our technique on the class of \emph{staircase} functions, which generalizes the ReLU, binarized, and quantized activation functions.
We then use results for staircase activation functions to obtain a separation method for general piecewise linear activation functions.
Empirically, using our strong formulation and separation technique, we can reduce the computational time in exact verification settings based on MIP and improve the false negative rate for inexact verifiers relying on the relaxation of the MIP formulation.
While originally developed for neural network formulation, the MIP formulation and its technical results draw heavily on classical theory in linear optimization, and may be of independent interest to other applications
Temporally Feathered Radiation Therapy under Uncertainty
No full textThis thesis focuses on radiation therapy planning through novel stochastic optimization models that account for biological heterogeneity and uncertainty in organ-at-risk (OAR) responses. Building on the temporally feathered radiation therapy (TFRT) strategy, we develop a personalized, biologically informed treatment framework that dynamically adjusts dose intensities and rest periods based on tissue-specific recovery potential. The model incorporates inter-organ variability using the linear-quadratic model within a dynamic normal tissue complication probability framework, allowing for tailored protection of sensitive tissues without compromising tumor coverage.
To address uncertainty in patient-specific parameters, we propose two stochastic models: a risk-neutral formulation that maximizes expected OAR recovery and a risk-averse approach minimizing worst-case toxicity using Conditional Value-at-Risk. A modified L-shaped algorithm is developed to solve the resulting nonconvex problems more efficiently than conventional solvers. Clinically, these models outperform standard intensity modulated radiation therapy, with the risk-averse version protecting critical structures such as the spinal cord and brainstem, and the risk-neutral version improving outcomes for the parotid and structures associated with speech and swallowing.
Because stochastic TFRT models can be computationally demanding as the number of scenarios increases, we introduce scenario generation techniques based on Wasserstein and fused Gromov-Wasserstein distances to approximate complex stochastic processes effectively. To solve the resulting models, we design block coordinate descent algorithms and demonstrate their performance across applications in stochastic TFRT, portfolio management, and capacity expansion
Integer Programming Approaches to Cancer Treatment: Objective Selection in Intensity-Modulated Radiation Therapy and Chemotherapy Treatment Design
No full textIn this thesis, we present multiple problems that arise in cancer treatment decision-making, and we analyze and implement methods used to solve them. The first problem is the selection of objectives that reflect latent clinical preferences during radiation therapy treatment. By connecting an inverse optimization formulation with greedy and regularized solution approaches, we show that sparse sets of objectives can be retrieved effectively. The development of the greedy forward selection approaches for objective selection leads to an in-depth exploration of the greedy algorithm's performance when the optimized set function is approximately submodular. In addition to the greedy algorithm, we study approximate submodularity in other areas in discrete optimization. The second cancer treatment problem is combination chemotherapy optimization, which requires merging differential equations that model dynamics together with discrete decision variables for complex operational constraints. We formulate this problem as a mixed-integer linear program and solve two models, one that focuses on tumor shrinkage and another that minimizes toxicity
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