Sublattices of lattices of order-convex sets, I. The main representation theorem

For a partially ordered set P, we denote by Co(P) the lattice of order-convex subsets of P. We find three new lattice identities, (S), (U), and (B), such that the following result holds. Theorem. Let L be a lattice. Then L embeds into some lattice of the form Co(P) iff L satisfies (S), (U), and (B). Furthermore, if L has an embedding into some Co(P), then it has such an embedding that preserves the existing bounds. If L is finite, then one can take P finite, of cardinality at most $2n^2-5n+4$, where n is the number of join-irreducible elements of L. On the other hand, the partially ordered set P can be chosen in such a way that there are no infinite bounded chains in P and the undirected graph of the predecessor relation of P is a tree

Sublattices of lattices of convex subsets of vector spaces

For a left vector space V over a totally ordered division ring F, let Co(V) denote the lattice of convex subsets of V. We prove that every lattice L can be embedded into Co(V) for some left F-vector space V. Furthermore, if L is finite lower bounded, then V can be taken finite-dimensional, and L embeds into a finite lower bounded lattice of the form $Co(V,Z)=\{X\cap Z | X\in Co(V)\}$, for some finite subset $Z$ of $V$. In particular, we obtain a new universal class for finite lower bounded lattices

Autoregressive Wild Bootstrap Inference for Nonparametric Trends

In this paper we propose an autoregressive wild bootstrap method to construct confidence bands around a smooth deterministic trend. The bootstrap method is easy to implement and does not require any adjustments in the presence of missing data, which makes it particularly suitable for climatological applications. We establish the asymptotic validity of the bootstrap method for both pointwise and simultaneous confidence bands under general conditions, allowing for general patterns of missing data, serial dependence and heteroskedasticity. The finite sample properties of the method are studied in a simulation study. We use the method to study the evolution of trends in daily measurements of atmospheric ethane obtained from a weather station in the Swiss Alps, where the method can easily deal with the many missing observations due to adverse weather conditions

Comparing linear discriminant analysis and supervised learning algorithms for binary classification - a method comparison study

In psychology, linear discriminant analysis (LDA) is the method of choice for two-group classification tasks based on questionnaire data. In this study, we present a comparison of LDA with several supervised learning algorithms. In particular, we examine to what extent the predictive performance of LDA relies on the multivariate normality assumption. As nonparametric alternatives, the linear support vector machine (SVM), classification and regression tree (CART), random forest (RF), probabilistic neural network (PNN), and the ensemble k conditional nearest neighbor (EkCNN) algorithms are applied. Predictive performance is determined using measures of overall performance, discrimination, and calibration, and is compared in two reference data sets as well as in a simulation study. The reference data are Likert-type data, and comprise 5 and 10 predictor variables, respectively. Simulations are based on the reference data and are done for a balanced and an unbalanced scenario in each case. In order to compare the algorithms' performance, data are simulated from multivariate distributions with differing degrees of nonnormality. Results differ depending on the specific performance measure. The main finding is that LDA is always outperformed by RF in the bimodal data with respect to overall performance. Discriminative ability of the RF algorithm is often higher compared to LDA, but its model calibration is usually worse. Still LDA mostly ranges second in cases it is outperformed by another algorithm, or the differences are only marginal. In consequence, we still recommend LDA for this type of application

LQVSumm: a corpus of linguistic quality violations in multi-document summarization

We present LQVSumm, a corpus of about 2000 automatically created extractive multi-document summaries from the TAC 2011 shared task on Guided Summarization, which we annotated with several types of linguistic quality violations. Examples for such violations include pronouns that lack antecedents or ungrammatical clauses. We give details on the annotation scheme and show that inter-annotator agreement is good given the open-ended nature of the task. The annotated summaries have previously been scored for Readability on a numeric scale by human annotators in the context of the TAC challenge; we show that the number of instances of violations of linguistic quality of a summary correlates with these intuitively assigned numeric scores. On a system-level, the average number of violations marked in a system’s summaries achieves higher correlation with the Readability scores than current supervised state-of-the-art methods for assigning a single readability score to a summary. It is our hope that our corpus facilitates the development of methods that not only judge the linguistic quality of automatically generated summaries as a whole, but which also allow for detecting, labeling, and fixing particular violations in a text

Bootstrap inference for environmental trends

In this thesis, statistical methods were developed to analyse time trends when a considerable part of the data series is missing. Missing observations complicate the analysis because many existing methods are not directly applicable in this situation. When studying climate time series, however, missing data are a frequently encountered problem due to, for example, maintenance of equipment or adverse weather conditions that prevent measurements from being taken. In addition, this research investigated how to model (economic) relationships that are changing over time. In both approaches, the focus lies on finding an accurate measure of uncertainty around the estimates produced with the help of the model. The methods developed in this thesis are used to study atmospheric ethane which is an indirect greenhouse gas contributing to global warming. The dissertation shows that atmospheric ethane has experienced a recent upward trend when the observations were obtained in the Northern Hemisphere. Time series coming from measurement stations located in the Southern Hemisphere do not show the same pattern

Governance in socioeconomic pathways and its role for future adaptive capacity

Governance is one of the critical components for sustainability, but quantification within scenarios and projections of future socioeconomic development has been lacking. This analysis of various pathways looks at how best to overcome 'weak' governance and strengthen adaptive capacity. Weak governance is one of the key obstacles for sustainable development. Undoubtedly, improvement of governance comes with a broad range of co-benefits, including countries' abilities to respond to pressing global challenges such as climate change. However, beyond the qualitative acknowledgement of its importance, quantifications of future pathways of governance are still lacking. This study provides projections of future governance in line with the Shared Socioeconomic Pathways. We find that under a 'rocky road' scenario, 30% of the global population would still live in countries characterized by weak governance in 2050, while under a 'green road' scenario, weak governance would be almost entirely overcome over the same time frame. On the basis of pathways for governance, we estimate the adaptive capacity of countries to climate change. Limits to adaptive capacity exist even under optimistic pathways beyond mid-century. Our findings underscore the importance of accounting for governance in assessments of climate change impacts

Avaliação psicológica como fator protetor à interrupção de tratamento na psicoterapia psicanalítica de crianças: dados empíricos

A avaliação psicológica pode fornecer informações importantes sobre sintomas, personalidade, diagnóstico e prognóstico do paciente, auxiliando no desenvolvimento de estratégias terapêuticas. Esta coorte retrospectiva objetivou determinar se há associação entre a realização de avaliação psicológica antes da psicoterapia e a permanência dos pacientes em tratamento. Foram analisados prontuários de 2.106 crianças atendidas em dois ambulatórios de saúde mental em Porto Alegre. Crianças que haviam realizado avaliação psicológica antes de iniciar a psicoterapia apresentavam 65% mais chance de aderir ao tratamento e 44% menos chance de abandoná-lo do que crianças que não haviam realizado avaliação psicológica. A avaliação psicológica auxilia os pais a se engajarem no tratamento de suas crianças, tornando-o menos coercitivo, além de fornecer uma fundamentação mais concreta para o subjetivo processo psicoterapêutico

Accounting for socioeconomic constraints in sustainable irrigation expansion assessments

Sustainable irrigation expansion over water limited croplands is an important measure to enhance agricultural yields and increase the resilience of crop production to global warming. While existing global assessments of irrigation expansion mainly illustrate the biophysical potential for irrigation, socioeconomic factors such as weak governance or low income, that demonstrably impede the successful implementation of sustainable irrigation, remain largely underexplored. Here we provide five scenarios of sustainable irrigation deployment in the 21st century integrated into the framework of Shared Socioeconomic Pathways, which account for biophysical irrigation limits and socioeconomic constraints. We find that the potential for sustainable irrigation expansion implied by biophysical limits alone is considerably reduced when socioeconomic factors are considered. Even under an optimistic scenario of socio-economic development, we find that additional calories produced via sustainable irrigation by 2100 might reach only half of the maximum biophysical potential. Regions with currently modest socioeconomic development such as Sub-Saharan Africa are found to have the highest potential for improvements. In a scenario of sustainable development, Sub-Saharan Africa would be able to almost double irrigated food production and feed an additional 70 million people compared to 2020, whereas in a scenario where regional rivalry prevails, this potential would be halved. Increasing sustainable irrigation will be key for countries to meet the projected food demands, tackle malnutrition and rural poverty in the context of increasing impacts of anthropogenic climate change on food systems. Our results suggest that improving governance levels for example through enhancing the effectiveness of institutions will constitute an important leverage to increase adaptive capacity in the agricultural sector.Bundesministerium für Bildung und Forschunghttp://dx.doi.org/10.13039/501100002347H2020Peer Reviewe