Small is Successful!?

This paper provides experimental evidence on exit behavior of asymmetrically sized firms in a duopoly with declining demand. We conduct three treatments: (a) The basic model with indivisible real capital. The structure of this treatment represents the main findings of Ghemawat and Nalebuff (1985); (b) an extension of the basic model by introducing a bankruptcy constraint; (c) here we allow for divisible real capital (Ghemawat and Nalebuff (1990)). In all three treatments we find behavior that is, by and large, in line with subgame perfect Nash Equilibrium. However, there is a problem of multiplicity of equilibria in (b) and we find an anchor effect as well as learning effects in (c).Exit, duopoly, declining market, experimental economics

Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin

In this paper we describe a dataset of German and Latin \textit{ground truth} (GT) for historical OCR in the form of printed text line images paired with their transcription. This dataset, called \textit{GT4HistOCR}, consists of 313,173 line pairs covering a wide period of printing dates from incunabula from the 15th century to 19th century books printed in Fraktur types and is openly available under a CC-BY 4.0 license. The special form of GT as line image/transcription pairs makes it directly usable to train state-of-the-art recognition models for OCR software employing recurring neural networks in LSTM architecture such as Tesseract 4 or OCRopus. We also provide some pretrained OCRopus models for subcorpora of our dataset yielding between 95\% (early printings) and 98\% (19th century Fraktur printings) character accuracy rates on unseen test cases, a Perl script to harmonize GT produced by different transcription rules, and give hints on how to construct GT for OCR purposes which has requirements that may differ from linguistically motivated transcriptions.Comment: Submitted to JLCL Volume 33 (2018), Issue 1: Special Issue on Automatic Text and Layout Recognitio

Terrestrial magma ocean solidification and formation of a candidate D" layer

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 31-34).In this thesis we investigate the solidification of early magma oceans on the Earth and the formation of a deep dense layer at the core-mantle boundary. We also study the concentrations and densities of the last layers of the solidified magma ocean and how they create a deep dense layer after solid-state overturn. The deep dense layer that forms in our model matches the bulk physical properties of the D" layer observed by other workers. This layer is also sufficiently dense that the bulk of its material is not reentrained by the mantle after the onset of convection, and that this layer is enriched in incompatible elements such as samarium and neodymium regardless of distribution coefficients used for incompatible elements in mantle minerals such as perovskite. However, we found that this probable D" layer is more enriched in samarium than is to be expected for a planet's mantle which evolves from an initially chondritic composition.by Alessondra Springmann.S.M

Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning

We combine three methods which significantly improve the OCR accuracy of OCR models trained on early printed books: (1) The pretraining method utilizes the information stored in already existing models trained on a variety of typesets (mixed models) instead of starting the training from scratch. (2) Performing cross fold training on a single set of ground truth data (line images and their transcriptions) with a single OCR engine (OCRopus) produces a committee whose members then vote for the best outcome by also taking the top-N alternatives and their intrinsic confidence values into account. (3) Following the principle of maximal disagreement we select additional training lines which the voters disagree most on, expecting them to offer the highest information gain for a subsequent training (active learning). Evaluations on six early printed books yielded the following results: On average the combination of pretraining and voting improved the character accuracy by 46% when training five folds starting from the same mixed model. This number rose to 53% when using different models for pretraining, underlining the importance of diverse voters. Incorporating active learning improved the obtained results by another 16% on average (evaluated on three of the six books). Overall, the proposed methods lead to an average error rate of 2.5% when training on only 60 lines. Using a substantial ground truth pool of 1,000 lines brought the error rate down even further to less than 1% on average.Comment: Submitted to JLCL Volume 33 (2018), Issue 1: Special Issue on Automatic Text and Layout Recognitio

The optimization air separation plants for combined cycle MHD-power plant applications

Some of the design approaches being employed during a current supported study directed at developing an improved air separation process for the production of oxygen enriched air for magnetohydrodynamics (MHD) combustion are outlined. The ultimate objective is to arrive at conceptual designs of air separation plants, optimized for minimum specific power consumption and capital investment costs, for integration with MHD combined cycle power plants

The costs of climate-change adaptation in Europe: A review

Climate change is expected to have significant impacts on Europe that will affect its economic sectors and the distribution of economic activity. While some of those climate-change impacts can be alleviated by mitigation action, some degree of climate change cannot be avoided anymore. This makes adaptation an essential component in addressing the impacts from climate change in the future. The purpose of this review is to compare recent estimates based on their adaptation perspective. This entails a detailed review of the methodologies used, but also of the definition of adaptation adopted. This review investigates those issues with a specific regional focus on Europe. At present, no study has explicitly and comprehensively estimated the overall costs of adapting Europe to climate change. Available are adaptation-cost estimates for industrialized countries in general, climate-change impact assessments for Europe, as well as several adaptation-cost or climate impact studies on the sector level. For industrialized countries, adaptation-investment needs are estimated to be USD 22-105 billion per year by 2030 (USD 16 billion without the construction sector). For Europe, climate-proofing new infrastructure is estimated to cost EUR 4.6-58 billion; and the economic impact of experiencing 2080s climate change today is valued at EUR 22-67 billion. In comparison, total investments in the EU are about two orders of magnitude larger (EUR 2.6 trillion in 2008). ..

Alteração de cor da coroa dental após a terapia endodôntica regenerativa: Revisão de literatura

TCC (graduação) - Universidade Federal de Santa Catarina. Centro de Ciências da Saúde. Odontologia.O objetivo deste estudo foi revisar, de modo sistematizado e por meio de casos clínicos, o efeito de materiais usados na terapia endodôntica regenerativa sobre a cor da coroa dental, bem como estudar os procedimentos para evitar ou reverter eventuais alterações de cor e os métodos utilizados para a determinação da cor. Para localização dos estudos, foi desenvolvida uma detalhada estratégia de busca para cada uma das seguintes bases de dados: Pubmed, Scopus e Web of Science. Foram utilizados termos de busca relacionados ao tema, tais como: alteração da cor dental, coloração, descoloração dental, estética, dente não vital, desvitalização pulpar, necrose pulpar, polpa dental, procedimento endodôntico regenerativo, regeneração, revitalização, revascularização, tratamento endodôntico regenerativo. As referências encontradas foram gerenciadas via software EndNoteTM Basic. Foram incluídos apenas estudos clínicos relacionados à cor da coroa de dentes humanos avaliados antes e após procedimentos endodônticos regenerativos. Cartas, capítulos de livros, resumos de conferências (anais) e revisões foram excluídos. Após a seleção por título/resumo, os artigos foram incluídos para leitura do texto completo, dos quais dados referentes à metodologia e aos resultados foram extraídos e sequencialmente analisados. Concluiu-se que a pasta triantibiótica, que contém minociclina, está mais relacionada à alteração de cor do que as demais medicações intracanal, e que a descoloração ocorreu independentemente do tipo de material utilizado no plug cervical, MTA branco ou MTA cinza. Nenhum método para prevenir ou reverter eventuais alterações de cor foi totalmente efetivo. O método mais utilizado para determinar a cor foi o visual

Transfer Learning for OCRopus Model Training on Early Printed Books

A method is presented that significantly reduces the character error rates for OCR text obtained from OCRopus models trained on early printed books when only small amounts of diplomatic transcriptions are available. This is achieved by building from already existing models during training instead of starting from scratch. To overcome the discrepancies between the set of characters of the pretrained model and the additional ground truth the OCRopus code is adapted to allow for alphabet expansion or reduction. The character set is now capable of flexibly adding and deleting characters from the pretrained alphabet when an existing model is loaded. For our experiments we use a self-trained mixed model on early Latin prints and the two standard OCRopus models on modern English and German Fraktur texts. The evaluation on seven early printed books showed that training from the Latin mixed model reduces the average amount of errors by 43% and 26%, respectively compared to training from scratch with 60 and 150 lines of ground truth, respectively. Furthermore, it is shown that even building from mixed models trained on data unrelated to the newly added training and test data can lead to significantly improved recognition results