Miracle-dz/UniFAR-FTUs huggingface dataset:
Decent Work and Burnout: A Profile Study With Academic Personnel

This research explores the relationship between Decent Work (DW) and Burnout in Portuguese and Brazilian academic personnel. We focus on identifying profiles resulting from the relationship between these variables. Seven hundred twenty-seven participants composed the sample (Portuguese = 334; Brazilian = 393), and data were collected online using the Decent Work Questionnaire (DWQ) and the Personal Burnout subscale from the Copenhagen Burnout Inventory (CBI). Results of multiple linear regressions showed that two DW dimensions were significant and negatively related to Burnout: Fundamental Principles and Values at Work and Adequate Working Time and Workload. We found four profiles by performing a cluster analysis: Low Decent Work; High Decent Work; and two other profiles with DW deficit in at least one DW dimension: Low Fundamental Principles and Values at Work and Low Adequate working time and workload. Moreover, 71% of the total sample showed some decent work deficit. Differences between Burnout and the DW dimensions were analyzed through a MANOVA. In our sample, considering the broad dimensions of DW, Burnout seems to be mainly dependent on the deficit of aspects related to the quality of treatment and interpersonal relationships at work (e.g., perceptions of fairness, participation, non-discrimination) as well as the balance of the workload and the adequacy of the working hours. Interventions aiming at improvements must focus on those two dimensions.
Population pharmacokinetics of esomeprazole in patients with preterm preeclampsia

Esomeprazole is a proton pump inhibitor being investigated for treatment of preeclampsia. Esomeprazole pharmacokinetics during pregnancy are unknown. We used data from 10 pregnant participants with preterm preeclampsia, and 49 non‐pregnant participants to develop a population pharmacokinetic model of esomeprazole. A two‐compartment model described the data well. In pregnant participants after single dose, clearance was 42.2% (14.9–61.6%) lower compared to non‐pregnant, most likely due to inhibition of CYP2C19. In non‐pregnant participants after repeated dosing, clearance was 54.9% (48.2–63.5%) lower in extensive metabolizers and bioavailability was 33% (10.0–52.0%) higher compared to single dosing, which could be due to autoinhibition of CYP2C19. During pregnancy, the CYP2C19 autoinhibition effect with repeated dosing is expected to lead to much lower increase in exposure compared to non‐pregnant individuals, since CYP2C19 is already inhibited due to pregnancy.
Routine buccal glucose reduces admission hypoglycaemia in premature newborns: A quality improvement project

Buccal glucose gel is an established treatment for hypoglycaemia in term and late preterm babies, and reduces admission to neonatal units. 1,2 Prophylactic use at 1 h of age in at- risk babies ≥35 weeks of results in less hypoglycaemia. 3 A Cochrane review has reported on the potential for prophylactic dextrose gel to reduce hypoglycaemia in at- risk infants and called for further studies including preterm babies. 4 There is little evidence for prophylactic use in preterm babies, but they remain vulnerable to severe hypoglycaemia. 5 This quality improvement project aimed to reduce the propor-tion of babies < 34 weeks with hypoglycaemia on admission to the neonatal unit by using prophylactic administration of glucose gel during initial stabilisation. No universally accepted definition for neonatal hypoglycaemia in preterm babies exists. In term babies, a blood sugar level < 1 mmol/L (18 mg/dL) is considered severe. 2 Preterm infants may not show clinical signs until hypoglycaemia is severe and persistent. It is thus normal practice to check a blood sugar level on admission and to ‘treat the number’ regardless of clinical signs. In this project, was defined and hypoglycaemia defined < mmol/L. Blood sugar level was on analyser (ABL90 A protocol for of 40% glucose during neonatal stabilisation Plan- study- act (PDSA) cycles
Magnetic Resonance Imaging or Computed Tomography for Suspected Acute Stroke: Association of Admission Image Modality with Acute Recanalization Therapies, Workflow Metrics, and Outcomes

To examine rates of intravenous thrombolysis (IVT), mechanical thrombectomy (MT), door‐to‐needle (DTN) time, door‐to‐puncture (DTP) time, and functional outcome between patients with admission magnetic resonance imaging (MRI) versus computed tomography (CT).
Watch Out for Burnout in COVID-19: A Greek Health Care Personnel Study

The aim of the current study was to examine the mental well-being of healthcare personnel (HCP) working in COVID-19 units in Greece and to calculate the prevalence of burnout (BO) amongst them. A questionnaire based on the Maslach Burnout Inventory for Medical Personnel was utilized between February 21st, 2021 and March 5th, 2021. A total of 190 HCP responded to the questionnaire, of which 73.7% were nurses and midwives. The mean age of the participants was 38.3 (8.4) years. Overall, 71.6% of the participants had a high BO score, while 20.5% had a moderate and 7.9% had a low BO score. Night shifts in COVID-19 wards and job dissatisfaction were significantly associated with a high BO score (P = .03 and P < .0001, respectively). The majority of HCP working in COVID-19 wards in Greece is experiencing high levels of overall BO and emotional exhaustion.
Key predictors of COVID-19 vaccine hesitancy in Malaysia: An integrated framework

Background As the vaccination drive against the coronavirus disease (COVID-19) in Malaysia progresses rapidly, the main challenge will gradually shift from procuring and distributing vaccines to ensuring the broadest possible acceptance among all population segments. Therefore, this study used the integrated framework of the health belief model (HBM) and the theory of reasoned action (TRA) to investigate the predictors of intention to receive COVID-19 vaccines in Malaysia. Methodology A market research company in Malaysia was engaged to collect data during June 11–20, 2021 using self-administered questionnaires via its online panel, ensuring a nationwide random sample of 804 respondents. A logistic regression was subsequently estimated to determine the significant predictors of vaccination intention. Results The predictors that significantly affect COVID-19 vaccine hesitancy in Malaysia are age, susceptibility, religious beliefs, attitude, subjective norms, and trust in the vaccine. In particular, those who are more inclined to get vaccinated are older individuals, have a higher perceived risk of infection and social pressure to get vaccinated, have a positive attitude, and have high levels of trust in the vaccine. Individuals’ who perceive that their religious beliefs are against vaccination are more likely to exhibit uncertainty toward it. Conclusion This study showed that although a large proportion of respondents indicated that they were willing to be vaccinated against COVID-19, there are concerns about religious barriers and trust in the vaccine that the relevant stakeholders should address. Campaigns should also focus on shaping the nation’s attitude toward COVID-19 vaccines, in parallel with encouraging people to use their social influence in helping those in their close circle who are unsure of vaccination to cross the line. These measures will prove to be pertinent as the nation begins to administer booster vaccines to tackle the waning effects of COVID-19 vaccines.
Toward a Global Definition and Understanding of Child Sexual Exploitation: The Development of a Conceptual Model

Child sexual exploitation (CSE) is a serious and persistent global issue affecting up to 5% of the child and youth population worldwide; yet there is no universally accepted definition. To develop a theoretically robust definition of CSE, this review systematically synthesized literature examining CSE definitions aiming to develop a conceptual model and typology. Electronic databases were searched to February 2021, yielding 384 nonduplicative records. Inclusion criteria were peer-reviewed and grey literature investigations of sexual exploitation, with a mean sample age of 18 years or younger, available in the English language. Literature review and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Sixty-six studies met final inclusion criteria. Two independent reviewers extracted relevant data and used an epistemological approach to thematically analyse meaning and patterns across CSE definitions. Key findings demonstrate that CSE nomenclature is widely inconsistent, and despite growing awareness of this severe form of abuse, language continues to perpetuate stigma and criminalisation, utilising terms such as ‘adolescent or child prostitute’. Our findings propose a scientifically and trauma-informed definition and conceptualisation of CSE, based on the following four-dimensional components: (1) A child/young person; (2) sexual acts; (3) abuse; and (4) exploitation (abuse + exchange). In this systematic review, a unified definition and conceptual model aims to advance knowledge and understanding of CSE, contributing to the progression of social norms which embrace nuances of trauma-informed practice and support for the identification and recovery of children, young people and families affected by sexual exploitation.
Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction

The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruction or approximation. It acts as a driving force for the development of several other greedy methods for sparse data reconstruction, and it also plays a vital role in the development of compressed sensing theory for sparse signal and image reconstruction. In this article, we propose the so-called dynamic orthogonal matching pursuit (DOMP) and enhanced dynamic orthogonal matching pursuit (EDOMP) algorithms which are more efficient than OMP for sparse data reconstruction from a numerical point of view. We carry out a rigorous analysis to establish the reconstruction error bound for DOMP under the restricted isometry property of the measurement matrix. The main result claims that the reconstruction error via DOMP can be controlled and measured in terms of the number of iterations, sparsity level of data, and the noise level of measurements. Moveover, the finite convergence of DOMP for a class of large-scale compressed sensing problems is also shown.
Flow-based Recurrent Belief State Learning for POMDPs

Partially Observable Markov Decision Process (POMDP) provides a principled and generic framework to model real world sequential decision making processes but yet remains unsolved, especially for high dimensional continuous space and unknown models. The main challenge lies in how to accurately obtain the belief state, which is the probability distribution over the unobservable environment states given historical information. Accurately calculating this belief state is a precondition for obtaining an optimal policy of POMDPs. Recent advances in deep learning techniques show great potential to learn good belief states. However, existing methods can only learn approximated distribution with limited flexibility. In this paper, we introduce the \textbf{F}l\textbf{O}w-based \textbf{R}ecurrent \textbf{BE}lief \textbf{S}tate model (FORBES), which incorporates normalizing flows into the variational inference to learn general continuous belief states for POMDPs. Furthermore, we show that the learned belief states can be plugged into downstream RL algorithms to improve performance. In experiments, we show that our methods successfully capture the complex belief states that enable multi-modal predictions as well as high quality reconstructions, and results on challenging visual-motor control tasks show that our method achieves superior performance and sample efficiency.
Microbial influencers: treating diabetes through the gut

1 Glycation, Nutrition and Metabolism Laboratory, Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, Australia 2 Department of Experimental Vascular Medicine, Amsterdam University Medical Center, Location AMC, Amsterdam Gastroenterology Endocrinology and Metabolism (AGEM) Institute, Amsterdam Institute for Infection and Immunity (AII), Amsterdam, The Netherlands 3 Department of Internal and Vascular Medicine, Amsterdam University Medical Center, Location AMC, Amsterdam, The Netherlands 4 Diabetes Centrum, Department of Internal and Vascular Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
Lung Function in Women With and Without Human Immunodeficiency Virus

Abstract Background Prior studies have found that human immunodeficiency virus (HIV) infection is associated with impaired lung function and increased risk of chronic lung disease, but few have included large numbers of women. In this study, we investigate whether HIV infection is associated with differences in lung function in women. Methods This was a cross-sectional analysis of participants in the Women’s Interagency HIV Study, a racially and ethnically diverse multicenter cohort of women with and without HIV. In 2018–2019, participants at 9 clinical sites were invited to perform spirometry. Single-breath diffusing capacity for carbon monoxide (DLCO) was also measured at selected sites. The primary outcomes were the post-bronchodilator forced expiratory volume in 1 second (FEV1) and DLCO. Multivariable regression modeling was used to analyze the association of HIV infection and lung function outcomes after adjustment for confounding exposures. Results FEV1 measurements from 1489 women (1062 with HIV, 427 without HIV) and DLCO measurements from 671 women (463 with HIV, 208 without HIV) met standards for quality and reproducibility. There was no significant difference in FEV1 between women with and without HIV. Women with HIV had lower DLCO measurements (adjusted difference, –0.73 mL/min/mm Hg; 95% confidence interval, −1.33 to −.14). Among women with HIV, lower nadir CD4 + cell counts and hepatitis C virus infection were associated with lower DLCO measurements. Conclusions HIV was associated with impaired respiratory gas exchange in women. Among women with HIV, lower nadir CD4 + cell counts and hepatitis C infection were associated with decreased respiratory gas exchange.
Computational Storytelling and Emotions: A Survey

Storytelling has always been vital for human nature. From ancient times, humans have used stories for several objectives including entertainment, advertisement, and education. Various analyses have been conducted by researchers and creators to determine the way of producing good stories. The deep relationship between stories and emotions is a prime example. With the advancement in deep learning technology, computers are expected to understand and generate stories. This survey paper is intended to summarize and further contribute to the development of research being conducted on the relationship between stories and emotions. We believe creativity research is not to replace humans with computers, but to find a way of collaboration between humans and computers to enhance the creativity. With the intention of creating a new intersection between computational storytelling research and human creative writing, we introduced creative techniques used by professional storytellers.
Informed Pre-Training on Prior Knowledge

When training data is scarce, the incorporation of additional prior knowledge can assist the learning process. While it is common to initialize neural networks with weights that have been pre-trained on other large data sets, pre-training on more concise forms of knowledge has rather been overlooked. In this paper, we propose a novel informed machine learning approach and suggest to pre-train on prior knowledge. Formal knowledge representations, e.g. graphs or equations, are first transformed into a small and condensed data set of knowledge prototypes. We show that informed pre-training on such knowledge prototypes (i) speeds up the learning processes, (ii) improves generalization capabilities in the regime where not enough training data is available, and (iii) increases model robustness. Analyzing which parts of the model are affected most by the prototypes reveals that improvements come from deeper layers that typically represent high-level features. This confirms that informed pre-training can indeed transfer semantic knowledge. This is a novel effect, which shows that knowledge-based pre-training has additional and complementary strengths to existing approaches.
CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models

In the field of Natural Language Processing, there are many tasks that can be tackled effectively using the cross-entropy (CE) loss function. However, the task of dialog generation poses unique challenges for CE loss. This is because CE loss assumes that, for any given input, the only possible output is the one available as the ground truth in the training dataset. But, in dialog generation, there can be multiple valid responses (for a given context) that not only have different surface forms but can also be semantically different. Furthermore, CE loss computation for the dialog generation task does not take the input context into consideration and, hence, it grades the response irrespective of the context. To grade the generated response for qualities like relevance, engagingness, etc., the loss function should depend on both the context and the generated response. To address these limitations, this paper proposes CORAL, a novel loss function based on a reinforcement learning (RL) view of the dialog generation task with a reward function that estimates human preference for generated responses while considering both the context and the response. Furthermore, to overcome challenges such as high sample complexity of RL training and a large action space, we propose a mix-policy training algorithm. Notably, using CORAL we can train dialog generation models without assuming the ground-truth as the only correct response. Extensive comparisons on benchmark datasets demonstrate that CORAL based models outperform strong state-of-the-art baseline models of different sizes.
MonoFormer: Towards Generalization of self-supervised monocular depth estimation with Transformers

Self-supervised monocular depth estimation has been widely studied recently. Most of the work has focused on improving performance on benchmark datasets, such as KITTI, but has offered a few experiments on generalization performance. In this paper, we investigate the backbone networks ( e.g. CNNs, Transformers, and CNN-Transformer hybrid models) toward the generalization of monocular depth estimation. We ﬁrst evaluate state-of-the-art models on diverse public datasets, which have never been seen during the network training. Next, we investigate the effects of texture-biased and shape-biased representations using the various texture-shifted datasets that we generated. We observe that Transformers exhibit a strong shape bias and CNNs do a strong texture-bias. We also ﬁnd that shape-biased models show better generalization performance for monocular depth estimation compared to texture-biased models. Based on these observations, we newly design a CNN-Transformer hybrid network with a multi-level adaptive feature fusion module, called MonoFormer. The design intuition behind MonoFormer is to increase shape bias by employing Transformers while compensating for the weak locality bias of Transformers by adaptively fusing multi-level representations. Extensive experiments show that the proposed method achieves state-of-the-art performance with various public datasets. Our method also shows the best generalization ability among the competitive methods.
Active Source Free Domain Adaptation

Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data. However, the SFDA setting faces an effect bottleneck due to the absence of source data and target supervised information, as evidenced by the limited performance gains of newest SFDA methods. In this paper, for the first time, we introduce a more practical scenario called active source free domain adaptation (ASFDA) that permits actively selecting a few target data to be labeled by experts. To achieve that, we first find that those satisfying the properties of neighbor-chaotic, individual-different, and target-like are the best points to select, and we define them as the minimum happy (MH) points. We then propose minimum happy points learning (MHPL) to actively explore and exploit MH points. We design three unique strategies: neighbor ambient uncertainty, neighbor diversity relaxation, and one-shot querying, to explore the MH points. Further, to fully exploit MH points in the learning process, we design a neighbor focal loss that assigns the weighted neighbor purity to the cross-entropy loss of MH points to make the model focus more on them. Extensive experiments verify that MHPL remarkably exceeds the various types of baselines and achieves significant performance gains at a small cost of labeling.
Detecting the impact of nuclear reactions on neutron star mergers through gravitational waves

Nuclear reactions may affect gravitational-wave signals from neutron-star mergers, but the impact is uncertain. In order to quantify the effect, we compare two numerical simulations representing intuitive extremes. In one case reactions happen instantaneously. In the other case, they occur on timescales much slower than the evolutionary timescale. We show that, while the differences in the two gravitational-wave signals are small, they should be detectable by third-generation observatories. To avoid systematic errors in equation of state parameters inferred from observed signals, we need to accurately implement nuclear reactions in future simulations.
Symmetry Teleportation for Accelerated Optimization

Existing gradient-based optimization methods update parameters locally, in a direction that minimizes the loss function. We study a different approach, symmetry teleportation, that allows parameters to travel a large distance on the loss level set, in order to improve the convergence speed in subsequent steps. Teleportation exploits symmetries in the loss landscape of optimization problems. We derive loss-invariant group actions for test functions in optimization and multi-layer neural networks, and prove a necessary condition for teleportation to improve convergence rate. We also show that our algorithm is closely related to second order methods. Experimentally, we show that teleportation improves the convergence speed of gradient descent and AdaGrad for several optimization problems including test functions, multi-layer regressions, and MNIST classification.
The distance to the border of a random tree

Given a Galton-Watson process conditioned to have total progeny equal to n, we study the asymptotic probability that this conditioned GaltonWatson process has distance to the border bigger or equal than k, as the number of nodes n → ∞. Through this paper we answer a question posed in [1]. A problem which is akin to this one was solved by Rényi and Szekeres for Cayley trees, [23], de Bruijn, Knuth, and Rice for plane trees, [6], and Flajolet, Gao, Odlyzko, and Richmond for binary trees, [9]. The distance to the border is dual, in a certain sense, to the height. The first of these distances is the minimum of the distances from the root to the leaves. The second is the maximum of the distances from the root to the leaves. These are two extreme complementary cases.