1,721,349 research outputs found
Machine Learning Techniques for Inverse Problems
No full textI problemi inversi sono il modello naturale per l'analisi di molte applicazioni del mondo reale. Esempi tipici sono la risonanza magnetica (MRI), la tomografia computerizzata a raggi X (CT) e problemi di recupero delle immagini. Un problema inverso consiste nel ricostruire una sorgente sconosciuta da osservazioni limitate e potenzialmente distorte. Le cosiddette tecniche ``data-driven'' per risolvere i problemi inversi sono diventate popolari negli ultimi anni grazie alla loro efficacia in molti scenari pratici. Tuttavia, ad oggi sono state fornite poche garanzie teoriche sul loro funzionamento. Questo manoscritto si propone di colmare queste lacune procedendo lungo diverse direzioni chiave.
Gli approcci data-driven sono state oggetto di attenzione poiché richiedono meno conoscenze a priori. Nel primo lavoro, proponiamo e studiamo un approccio di Statistical Learning, basato su Empirical Risk Minimization (ERM), per determinare parametri a partire da esempi. Il nostro principale contributo è un'analisi teorica che dimostra come, se il numero di esempi è abbastanza grande, questo approccio sia ottimale ed adattattivo al livello di rumore e alla regolarità della soluzione. Mostriamo l'applicabilità del nostro framework a una vasta classe di problemi inversi, inclusi i metodi di regolarizzazione spettrale e le norme che promuovono sparsità. Simulazioni numeriche supportano e illustrano ulteriormente i risultati teorici.
Inoltre, introduciamo un approccio data-driven per costruire operatori (fortemente) nonespansivi. Presentiamo l'utilità di tale tecnica nel contesto dei metodi Plug-and-Play, in cui un operatore prossimale in algoritmi classici come Forward-Backward Splitting o l'iterazione primale-duale di Chambolle--Pock viene sostituito da un operatore che mira ad essere fortemente nonespansivo. Stabiliamo un rigoroso quadro teorico per imparare tali operatori utilizzando un approccio ERM. Inoltre, deriviamo una soluzione che è garantita essere fortemente nonespansiva e affine a tratti nell'inviluppo convesso del training set. Dimostriamo che questo operatore converge alla migliore soluzione empirica aumentando il numero di punti all'interno dell'inviluppo. Infine, proponiamo una strategia di implementazione pratica e un'applicazione nel contesto dell'image denoising.
Spesso, i problemi data-driven si scontrano con la sfida di affrontare problemi di dimensione infinita. I teoremi di rappresentazione, introdotti nel contesto dei metodi kernel e recentemente estesi allo studio di problemi variazionali generali, possono essere applicati per affrontare questa questione. Questi teoremi caratterizzano le soluzioni di problemi di dimensione infinita come una combinazione convessa finita di un numero limitato di ``atomi''. In casi specifici, si può dimostrate che questi atomi sono i punti estremali di una palla unitaria specifica. In questo contesto, contribuiamo caratterizzando l'insieme dei punti estremali della palla unitaria delle funzioni Lipschitziane in spazi metrici finiti. Di conseguenza, verrà fornito un teorema di rappresentazzione in questa impostazione, generalizzando il cosiddetto Teorema di Minkowski-Carathéodory a spazi di dimensione infinita.Inverse problems serve as a general playground for analyzing many real-world applications. Typical examples are MRI, X-Ray CT, and image recovery. An inverse problem involves reconstructing an unknown source from limited and possibly distorted observations.
The so-called data-driven techniques for solving inverse problems have become popular in recent years due to their effectiveness in many practical scenarios. Yet, few theoretical guarantees have been provided to date. This manuscript aims to bridge this gap in several key directions.
Data driven approaches have gained attention since they require less prior knowledge. First, we propose and study a statistical machine learning approach, based on Empirical Risk Minimization, to determine the best regularization parameter given a finite set of examples. Our main contribution is a theoretical analysis, showing that, if the number of examples is big enough, this approach is optimal and adaptive to the noise level and the smoothness of the solution. We showcase the applicability of our framework to a broad class of inverse problems, including spectral regularization methods and sparsity-promoting norms. Numerical simulations further support and illustrate the theoretical findings.
Moreover, we introduce a data-driven approach for constructing (firmly) nonexpansive operators. We present the utility of such a technique in the context of Plug-and-Play methods, where one proximal operator in classical algorithms such as Forward-Backward Splitting or the Chambolle--Pock primal-dual iteration is substituted by an operator that aims to be firmly nonexpansive. We establish a rigorous theoretical framework for learning such operators using an ERM approach. Further, we derive a solution that is ensured to be firmly nonexpansive and piecewise affine in the convex envelope of the training data. We prove that such an operator converges to the best empirical solution when increasing the number of points inside the envelope. Finally, we propose a practical implementation strategy and an application in the context of image denoising.
Often, data-driven approaches require to deal with infinite-dimensional problems. Representer theorems, introduced in the context of kernel methods, and recently extended for studying general variational problems, can be applied for tackling this issue. These theorems characterize solutions of infinite-dimensional problems as a finite convex combination of a limited number of ``atoms''. In specific cases, these atoms can be shown to be the extreme points of a specific unit ball. In this setting, we contribute by characterizing the set of extreme points of the Lipzchitz unit ball in finite metric spaces. Consequently, a representer theorem in this setting will be provided, generalizing the so-called Minkowski-Carathéodory Theorem to infinite-dimensional spaces
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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