Public, Tax, and Health Policies and Institutional Performance

This dissertation evaluates the effectiveness of public interventions in tax policy (such as a tax compliance campaign in Greece), the performance of public institutions that dictate land zoning (corruption of zoning officials in Greece and Spain) and public health (publicly-provided health insurance; namely, Medicaid). The common underlying theme of the dissertation is the public nature of the policies examines with an empirical emphasis. The ultimate goal of this research body is to provide credible policy solutions for the improvement of public administration

Competence-based Curriculum Learning for Neural Machine Translation

Current state-of-the-art NMT systems use large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized learning rate schedules and large batch sizes. This is undesirable as it requires extensive hyperparameter tuning. In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance. Our framework consists of a principled way of deciding which training samples are shown to the model at different times during training, based on the estimated difficulty of a sample and the current competence of the model. Filtering training samples in this manner prevents the model from getting stuck in bad local optima, making it converge faster and reach a better solution than the common approach of uniformly sampling training examples. Furthermore, the proposed method can be easily applied to existing NMT models by simply modifying their input data pipelines. We show that our framework can help improve the training time and the performance of both recurrent neural network models and Transformers, achieving up to a 70% decrease in training time, while at the same time obtaining accuracy improvements of up to 2.2 BLEU

Generative Design in Minecraft (GDMC), Settlement Generation Competition

This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map - ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018 proceedings, as part of the workshop on Procedural Content Generatio

The Social Appearance Anxiety Scale in Italian adolescent populations: Construct validation and group discrimination in community and clinical eating disorders samples

Anxiety in situations where one’s overall appearance (including body shape) may be negatively evaluated is hypothesized to play a central role in Eating Disorders (EDs) and in their co-occurrence with Social Anxiety Disorder (SAD). Three studies were conducted among community (N = 1995) and clinical (N = 703) ED samples of 11- to 18-year-old Italian girls and boys to (a) evaluate the psychometric qualities and measurement equivalence/invariance (ME/I) of the Social Appearance Anxiety (SAA) Scale (SAAS) and (b) determine to what extent SAA or other situational domains of social anxiety related to EDs distinguish adolescents with an ED only from those with SAD. Results upheld the one-factor structure and ME/I of the SAAS across samples, gender, age categories, and diagnostic status (i.e., ED participants with and without comorbid SAD). The SAAS demonstrated high internal consistency and 3-week test–retest reliability. The strength of the inter-relationships between SAAS and measures of body image, teasing about appearance, ED symptoms, depression, social anxiety, avoidance, and distress, as well as the ability of SAAS to discriminate community adolescents with high and low levels of ED symptoms and community participants from ED participants provided construct validity evidence. Only SAA strongly differentiated adolescents with any ED from those with comorbid SAD (23.2 %). Latent mean comparisons across all study groups were performed and discussed

Nausea and Vomiting of Early Pregnancy

Nausea and vomiting in early pregnancy is very common. The severest form, hyperemesis gravidarum,is important as mismanagement can lead to Wernicke’s encephalopathy, central pontine myelosis and death. There is a lack of high-quality evidence in the management of nausea and vomiting in early pregnancy and in the safety of the commonly used drugs, especially the reported side effects and their management. Pregnancies following bariatric surgery are becoming more frequent and care should be taken managing nausea and vomiting in this group since thiamine is primarily absorbed in the small intestine, and Wernicke’s encephalopathy has been described following some types of bariatric surgery. Severe cases of hyperemesis gravidarum warrant caution as Wernicke’s has been described following total parenteral nutrition,and it must be remembered that thiamine needs to be supplemented in this group. The etiology remains unknown and there is scope for research in this area

A new graph parameter related to bounded rank positive semidefinite matrix completions.

The Gram dimension gd(G) of a graph G is the smallest inte- ger k ≥ 1 such that any partial real symmetric matrix, whose entries are specified on the diagonal and at the off-diagonal positions corresponding to edges of G, can be completed to a positive semidefinite matrix of rank at most k (assuming a positive semidefinite completion exists). For any fixed k the class of graphs satisfying gd(G) ≤ k is minor closed, hence it can characterized by a finite list of forbidden minors. We show that the only minimal forbidden minor is Kk+1 for k ≤ 3 and that there are two minimal forbidden minors: K5 and K2,2,2 for k = 4. We also show some close connections to Euclidean realizations of graphs and to the graph parameter ν=(G) of [21]. In particular, our characterization of the graphs with gd(G) ≤ 4 implies the forbidden minor characterization of the 3-realizable graphs of Belk and Connelly [8,9] and of the graphs with ν=(G) ≤ 4 of van der Holst [21]

Growth And Variability Of School District Income Tax Revenues: Is Tax Base Diversification A Good Idea For School Financing?

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145497/1/coep12276_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145497/2/coep12276.pd

Positive semidefinite matrix completion, universal rigidity and the Strong Arnold Property

This paper addresses the following three topics: positive semidefinite (psd) matrix completions, universal rigidity of frameworks, and the Strong Arnold Property (SAP). We show some strong connections among these topics, using semidefinite programming as unifying theme. Our main contribution is a sufficient condition for constructing partial psd matrices which admit a unique completion to a full psd matrix. Such partial matrices are an essential tool in the study of the Gram dimension \gd(G) of a graph G, a recently studied graph parameter related to the low psd matrix completion problem. Additionally, we derive an elementary proof of Connelly's sufficient condition for universal rigidity of tensegrity frameworks and we investigate the links between these two sufficient conditions. We also give a geometric characterization of psd matrices satisfying the Strong Arnold Property in terms of nondegeneracy of an associated semidefinite program, which we use to establish some links between the Gram dimension \gd(\cdot) and the Colin de Verdi\`ere type graph parameter ν=(⋅)