Miracle-dz/UniFAR-FTUs huggingface dataset:
The completion of locally refined simplicial partitions created by bisection

Recently, in [Found. Comput. Math., 7(2) (2007), 245-269], we proved that an adaptive finite element method based on newest vertex bisection in two space dimensions for solving elliptic equations, which is essentially the method from [SINUM, 38 (2000), 466-488] by Morin, Nochetto, and Siebert, converges with the optimal rate. The number of triangles N in the output partition of such a method is generally larger than the number M of triangles that in all intermediate partitions have been marked for bisection, because additional bisections are needed to retain conforming meshes. A key ingredient to our proof was a result from [Numer. Math., 97(2004), 219-268] by Binev, Dahmen and DeVore saying that N - N-0 <= CM for some absolute constant C, where N-0 is the number of triangles from the initial partition that have never been bisected. In this paper, we extend this result to bisection algorithms of n-simplices, with that generalizing the result concerning optimality of the adaptive finite element method to general space dimensions.
Development and Validation of the Greek version of the Early Childhood Oral Health Impact Scale (ECOHIS)

Department of Community and Preventive Dentistry, School of Dentistry, National and Kapodistrian University of Athens, Athens, Greece Department of Pediatric Dentistry, School of Dentistry, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States Department of Pediatric Dentistry, School of Dentistry, University of Louisville, Kentucky, KY, United States
Addressing The False Negative Problem of Deep Learning MRI Reconstruction Models by Adversarial Attacks and Robust Training

Deep learning models have been shown to be successful in accelerating MRI reconstruction, over traditional methods. However, it has been observed that these methods tend to miss rare small features, such as meniscal tears, subchondral osteophyte, etc. in musculoskeletal applications. This is a concerning ﬁnding as these small and rare features are the particularly relevant in clinical diagnostic settings. Additionally, such potentially dangerous loss of details in the reconstructed images are not reﬂected by global image ﬁdelity metrics such as mean-square error (MSE) and structural similarity metric (SSIM). In this work, we propose a framework to ﬁnd the worst-case false negatives by adversarially attacking the trained models and improve the models’ability to reconstruct the small features by robust training.
Ovine Abortion Associated with Campylobacter fetus subsp. fetus ST2 in Turkey

In this study, we aimed to evaluate the microbiological, molecular and pathological findings of abortus cases detected in a sheep herd consist of 200 animals. Macroscopically, irregular necrotic foci were observed in the liver in aborted fetuses. Selective and non-selective media were used for the isolation of causative agent. Phenotypic and molecular tests were performed for identification. Campylobacter fetus subsp. fetus (Cff) was isolated from organs of four foetuses aborted. While the vaginal swap samples taken from sheep that did not abort, water, feed and litter samples produced negative results for Cff, 5 of the 20 faecal samples and 5 of the vaginal swaps taken from the sheep that had aborted, yielded Cff. Multilocus Sequence Typing (MLST) was used for genotyping and all isolates were detected as Sequence Type 2 (ST2). This is the first documented report of an ovine abortion caused by Cff ST2 in Turkey. It is considered that the development and use of vaccines, containing local Campylobacter species, would contribute to both prophylaxis and control of abortions caused by campylobacters including Campylobacter fetus subsp. fetus.
Modeling the Potential Distribution of Machilus thunbergii under the Climate Change Patterns in China

The potential geographic distribution and favorable climatic conditions of Machilus thunbergii under current and future predicted climates in China are predicted based on MaxEnt model and ArcGIS software. The results show that the AUC values in different time periods and emission seniors are more than 0.9, which indicates the prediction is excellent. Precipitation of the coldest quarter, precipitation of the driest month, annual precipitation, mean diurnal range, and temperature annual range are the most important environmental factors affecting the distribution of Machilus thunbergii. At present, the suitable areas of Machilus thunbergii are mainly concentrated in the eastern subtropics of China, with a total area of 118.47 × 104 km2. The medium-suitability area and the high-suitability area are concentrated in Wuyi Mountains, Luoxiao Mountains, Xuefeng Mountains, Nanling and east of Taiwan Mountains. With the change of climate, the suitable area increases, and the medium-suitability area and high-suitability area migrate and expand to the east, and the low-suitability area expands slightly to the west and north. So, the simulated distribution of Machilus thunbergii should be one of priorities, when instigating in-situ conservation. The research results can provide a theoretical reference for the popularization and planting of Machilus thunbergii.
4|2020 Firm Productivity, Wages, and Sorting

Increasing wage inequality is associated with changes in the degree of labor market sorting, i.e. the allocation of workers to firms. To measure sorting, we propose a new method which disentangles the respective contributions of worker and firm heterogeneity to wage inequality. Inspired by sorting theory, we infer firm productivity from estimating firm-level production functions, taking into account that worker ability and firm productivity may interact at the match level. Using German data, we find that highly productive firms display low labor shares, dominate concentratedmarkets, and pay lowerwages than less productive firms. Sorting is positive, but lower than what wage-based measures suggest. It increases over time, drivenbynewmatchesbetween low-productivity firmsand low-abilityworkers. At the top, sorting decreases, reflected in worker transitions away from high-productivity firms that pay relatively low wages. We discuss implications of our findings for the interpretation of increasing wage inequality. Zusammenfassung Wachsende Lohnungleichheit geht mit Veränderungen der Allokation von Arbeitnehmern zu Arbeitgebern imArbeitsmarkteinher.Dies spiegelt sichauch ineinemsteigendenSortierungsgrad des Arbeitsmarkts wider. Wir entwickeln eine neue Methode zur Messung dieses Sortierungsgrades, welche die jeweiligen Beiträge von Arbeitnehmerund Arbeitgeberheterogenität zur Lohnungleichheit entflechtet. Inspiriert von theoretischenModellenderArbeitsmarktsortierung leiten wir die Produktivität der arbeitgebenden Unternehmen aus Schätzungen von Produktionsfunktionen auf der Firmenebene ab. Wir berücksichtigen dabei insbesondere, dass die Firmenproduktivität auf der Matchebene mit der Leistungsfähigkeit der einzelnen Arbeitnehmer interagieren könnte. Anhand deutscher Daten beobachtenwir, dass hochproduktive Firmenniedrige Lohnquoten aufweisen, in konzentriertenMärktenoperierenund geringere Löhne zahlen als weniger produktive Firmen. Der Sortierungsgrad ist positiv aber niedriger als lohnbasierte Maße nahelegen. Er steigt mit der Zeit, getrieben durch neue Matches zwischen relativ unproduktiven Firmen und weniger leistungsfähigen Arbeitnehmern. An der Spitze geht der Sortierungsgrad zurück, was sich darin widerspiegelt, dass Arbeitnehmer die produktivsten Firmen, die relativ geringe Löhne zahlen, verlassen. Wir diskutieren Implikationen unserer Ergebnisse für die Interpretation steigender Lohnungleichheit. JEL J24, J31, J40, J62, J64, L25
Few-Shot Learning on Google Landmark Challenge

Large amount of landmarks and extreme imbalance among classes have posed unique challenges in landmark recognition. In this project, we investigated the performance of different few-shot learning classiﬁcation models on Google Landmark Challenge dataset. We utilized three milestone models in the development of few-shot learning through convolutional neural networks: Siamese Network, Prototypical Network, and MetaOptNet. The analysis of our experiments shows that increasing number of classes during training will boost the performance of the models, while reducing number of examples per class will lower the performance. We found that Prototypical Network with Res-18 as baseline outperforms all the other models.
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

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The human microbiome: A promising target for lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide, and insights into its underlying mechanisms as well as potential therapeutic strategies are urgently needed. The microbiome plays an important role in human health, and is also responsible for the initiation and progression of lung cancer through its induction of inflammatory responses and participation in immune regulation, as well as for its role in the generation of metabolic disorders and genotoxicity. Here, the distribution of human microflora along with its biological functions, the relationship between the microbiome and clinical characteristics, and the role of the microbiome in clinical treatment of lung cancer were comprehensively reviewed. This review provides a basis for the current understanding of lung cancer mechanisms with a focus on the microbiome, and contributes to future decisions on treatment management.
Improving Variational Auto-Encoders using Householder Flow

Variational auto-encoders (VAE) are scalable and powerful generative models. However, the choice of the variational posterior determines tractability and flexibility of the VAE. Commonly, latent variables are modeled using the normal distribution with a diagonal covariance matrix. This results in computational efficiency but typically it is not flexible enough to match the true posterior distribution. One fashion of enriching the variational posterior distribution is application of normalizing flows, i.e., a series of invertible transformations to latent variables with a simple posterior. In this paper, we follow this line of thinking and propose a volume-preserving flow that uses a series of Householder transformations. We show empirically on MNIST dataset and histopathology data that the proposed flow allows to obtain more flexible variational posterior and competitive results comparing to other normalizing flows.
At Random

There is no rigorous answer. Convention: X denotes random variable (RV) and x denotes a value which X may take. We say that X is random if it may take value at random (notice, this is not a good de ̄nition because 'at random' is not de ̄ned precisely). In other words, we say X is random if there is no algorithm by which we can predict values of X: We say that x is a realization of X: Example. X is household income in the US, and x is the income of your family. Please give examples of other random variables and their realizations.
Evaluation of color descriptors for object and scene recognition

Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used. To increase illumination invariance and discriminative power, color descriptors have been proposed only recently. As many descriptors exist, a structured overview of color invariant descriptors in the context of image category recognition is required.
Effective Games in Multilayer Complex Networks

Understanding the mechanisms behind the origin and maintenance of cooperation has been the focus of much research during the last decades. In that context, the underlying structure of social interactions has been shown to greatly influence the chances of reaching high levels of cooperation. However, real networked systems are often shaped by multiple interdependencies that are not conveniently captured by a single network. For instance, in social networks, individuals can be connected through different types of relationships — originating from collaboration, professional, friendship, or family ties — which can be conveniently described by a multilayer network. In this thesis, we analyze how a population structured as a multilayer network of interactions alters the chances of reaching cooperation. In particular, we aim to understand under which conditions networked interactions effectively transform, globally, the social dilemmas of cooperation that individuals locally face. To that end, we implement a novel numerical tool that allows us to track the self-organization of cooperation in networked populations with an arbitrary number of layers. Our results show that interactions in multiple layers can transform the original dilemmas, creating new basins of attraction and stable equilibria, absent in a single type of network. Finally, we show that these game transformations are not trivial. Cooperation may either increase or decrease, depending on factors such as the number of layers, the strength of the dilemma, the topology of the network, or the level of degree overlap among layers.
Using Gamification for Reducing Infections in Hospitals

Hospital acquired infections are one of the biggest problems healthcare field is facing, which can end up in an increasing quantity of deaths, extra-days of hospital stay and costs for both hospital and patient. Performing hand hygiene is a simple and inexpensive prevention measure, but healthcare workers compliance with it is still far from desired. Direct observation is an effective but costly and time-consuming approach to assess healthcare workers' performance. This paper proposes a gamification solution for providing healthcare workers with feedback regarding their hand hygiene habits, aiming at change their behavior and optimize their performance. We went through two iterations of a Design Science Research Methodology, whose activities were performed on an intensive care unit of a Portuguese hospital. Nurses working on this unit were our focus group throughout the whole research. The proposal was evaluated using a process designed according to a DSRM evaluation framework. Despite the identified indoor location technologies’ limitations, which made it impossible to fully test the solution, we were able to conceptually validate the proposal, which appears to be promising in improving nurses’ awareness.
Some Engineering and Chemical Properties of Cooked Locust Bean Seed (Parkia biglobosa)

This study was conducted to determine effect of cooking duration on some engineering and chemical properties of locust bean seed. The locust bean seed was cooked for 1, 2, 3, 4, 5, and 6 hours. Length, breadth, and thickness of locust bean were 10.38 to 12.52 mm, 8.61 to 10.04 mm and 4.79 to 5.83 mm, respectively. Bulk density ranged from 0.77 to 1.62 g/cm. Cooking of the bean increased moisture content from 4.4 to 61.4%. Compressive forces required reaching the yield, breaking and peak point of the samples ranged from 10.22 to 211.26 N, 51.70 to 384.39 N, and 51.88 to 385.87 N, respectively. Young modulus decreased with increased cooking time while deformation increased. Cooking has significant influence on chemical properties at 5% level of significance. Thermal conductivity of un-cooked locust bean increased from 0.22 to 0.52 W/mK and specific heat increased from 1.92 to 2.60 kJ/kgK.
Researching mobile-assisted Chinese-character learning strategies among adult distance learners

ABSTRACT In the field of teaching and learning Chinese as a foreign language (CFL), most studies investigate Chinese character learning strategies in pen-and-paper study by campus-based students. With the increase in distance-learning, and expanding popularity of smartphones and tablets and widespread availability of mobile applications for language learning, it is now important to research mobile-assisted character learning strategies by distance learners. This study attempts to (i) identify changing practices when character learning is assisted by mobile technology; and (ii) analyse the underlying causes for the changing practices. These issues were investigated using an online survey, learning diary and email interviews with UK-based distance learners of beginners’ Chinese (n = 22). The findings suggest that there are important emerging new practices and previously unreported strategies such as constantly testing oneself and using pinyin input to identify new characters. They also reveal two strategy types not reported in previous research concerning social and affective dimensions. The study thereby provides important new insights into how adult distance learners of Chinese at beginners’ level learn characters when assisted by mobile technology, with significant pedagogical implications for Chinese character pedagogy in terms of course design and learner support.
Background: Cultivating self-compassion is increasingly recognized as a powerful method to regulate hyperactive threat-processes like shame and self-criticism, but fear of self-compassion

Background: Cultivating self-compassion is increasingly recognized as a powerful method to regulate hyperactive threat-processes like shame and self-criticism, but fear of self-compassion (FSC) can inhibit this. These difficulties are under-explored in personality disorder (PD) despite their prevalence. Furthermore, little evidence exists regarding how these factors relate to adverse childhood events (ACEs) and attachment. Method: 53 participants with a diagnosis of PD completed measures including childhood abuse/neglect, invalidation, early warmth, selfcompassion, shame, self-criticism, FSC, and anxious/avoidant attachment. Results: Selfcompassion was predicted uniquely by low early warmth; self-inadequacy by invalidation and abuse; whereas FSC was predicted by multiple ACEs. FSC and self-compassion were significantly correlated with self-criticism and shame, but not with one another. Conclusions: Low self-compassion and high FSC appear to be distinct problems, substantiating physiological models proposing distinct threat and soothing systems. Results are consistent with theories positing that low self-compassion has distinct origins to shame, self-criticism and FSC.
Migrant health literacy in the European Union: A systematic literature review

Objective: The European Union (EU) has experienced an increasing intake of migrants in recent years. As its population diversifies, new challenges present themselves for healthcare delivery due to communication, cultural differences and health literacy of migrants. In the light of this, this systematic literature review examines the state of the art of migrant health literacy research within the EU. Methods: Based on PRISMA guidelines, a systematic literature review was conducted in 2013, 2016 and 2018 using the following databases: PubMed, Web of Science, CINAHL, MEDLINE and PsychINFO. Results: A total of 21 articles were selected for inclusion. EU-related migrant health literacy research dated from 2009, but only a small number of the EU member states were represented. Eight general research themes were identifiable. Definitions of migrants varied, however, or could not be identified within a particular study, which may hamper the further dissemination of research, policy and capacity building. Conclusion: The review shows the evolution of migrant health literacy research in the EU. More research is needed however to support health system responses, to increase the health literacy of migrants and to improve the quality of healthcare in the EU.
Maternal General Health Status during Pregnancy and Adverse Birth Outcomes: Exposure Outcome in a Prospective Study

The prevalence of LBW is varies among different regions and countries, so that 95% of LBW occurs in Low and Middle Income Countries (LMICs), especially in vulnerable populations, studies have investigated the effect of psychosocial factors such as; mental health, anxiety and depression on the perinatal outcomes in developing countries. Therefore, in this study we evaluated the maternal health situation and its association with adverse pregnancy complications. A Prospective cohort study was conducted in nine health center and health stations during March 2014 to Jun 2015 in Tehran city. 532 pregnant women who are interested to participating in the study were selected. In order general health questionnaire was used with 28 questions (GHQ-28), to measure the health status of pregnant mothers. In each center health centers midwife specialists, extracted lists of pregnant women in the period of 1 to 3 months of pregnancy. The data collected, entered in the Stata-V11 statistical software. Mean age of mothers was 29.3 years, Frequency of LBW and PTB in <19, 20-35, 35< age groups was 8.33, 7.84, 8.33 and 16.67, 3.81, 8.33 respectively. Risk of preterm birth in pregnant women with mild, moderate and severe symptoms of General health problems against normal pregnant women, in three innings was; RR=2.84, RR=2.40, RR=2.72 respectively, Risk of low birth weight in pregnant women with mild, moderate and severe symptoms of General health problems against normal pregnant women, in three innings was; RR=2.02, RR=1.82, RR=2.63 respectively. In this study, crude odds ratio for the relationship between low birth weight and premature birth in pregnant women with general health status were significant, however LBW association with maternal health status is ambiguous, but the PTB can be said that there is a positive correlation. Considering the result of previous studies and our estimates in this study seems to maternal health status during pregnancy is a risk factor for adverse outcomes and it is suggested that to improve maternal health during pregnancy be given more importance.