Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Using Meditation Techniques for Children with Chronic Kidney Disease on Hemodialysis Therapy

Mindfulness meditation is a practice that allows one to purposefully pay attention to the present moment without judgment. It helps us to look at things with a new lens and typically change the way to see things. Aim of the study: the aim of the study was to evaluate the effect of the meditation Technique on the psychological and physical body change in children with chronic kidney disease on hemodialysis. Design: A quasi-experimental research design was used to conduct the study. Settings: This study was carried out in pediatric dialysis unit, Benha university Hospital. Sample: A purposive sample of children (47) undergoing hemodialysis therapy through 4 months period in the previously mentioned setting. Tools of data collection: Three tools were used; Tool I:-A structured interviewing questionnaire: Tool II: Pain measurement scale. Tool III: Child and adolescent mindfulness measure. Results of this study revealed that, a highly statistically significant difference of the studied children physiological and psychological change in post application meditation technique. This study concluded that: All children who practice meditation technique experienced significantly less anxiety, depression and experienced decrease in total pain level scores. The study recommended that: Although the effects in this study were not as robust as we had hoped, we still believe mindful meditation to be a promising intervention for children with chronic kidney disease on hemodialysis at different settings.
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators

Although few-shot learning research has advanced rapidly with the help of metalearning, its practical usefulness is still limited because most of them assumed that all meta-training and meta-testing examples came from a single domain. We propose a simple but effective way for few-shot classification in which a task distribution spans multiple domains including ones never seen during meta-training. The key idea is to build a pool of models to cover this wide task distribution and learn to select the best one for a particular task through cross-domain meta-learning. All models in the pool share a base network while each model has a separate modulator to refine the base network in its own way. This framework allows the pool to have representational diversity without losing beneficial domain-invariant features. We verify the effectiveness of the proposed algorithm through experiments on various datasets across diverse domains.
A STUDY OF CONSUMER BEHAVIOUR TOWARDS ISLAMIC HOTEL: ASSESSING ON KNOWLEDGE AND RELIGIOSITY

Islamic Hotel becomes a Blue Ocean strategy for Hotel industry. The demand for this market has been created by changes in consumer behavior. Therefore, this study has been conducted to examine the relationship between knowledge and religiosity on attitude of consumers towards Islamic Hotel. A group of Muslim consumers has been selected and the results show that the consumer’s decision on Islamic Hotel is influenced by knowledge and religiosity. The result also shows that there is a significance relationship between knowledge and religiosity towards consumer behavior on Islamic Hotel. The result of this study will help the Hotel provider in promoting their Islamic Hotel and becomes a guideline for government in designing the strategy or method to encourage hotelier to convert their conventional hotel to Islamic Hotel concept.
Tobacco Smoking and Use of Smokeless Tobacco and Their Association with Psychological Distress and Other Factors in a Rural District in Bangladesh: A Cross-Sectional Study

Background Tobacco smoking and use of smokeless tobacco are the most preventable cause of death in Bangladesh. The prevalence of psychological distress is increasing globally. This paper reports the smoking status and their association with psychological distress and other factors in a rural district, Narail, of Bangladesh. Materials and Methods Data were collected from 2425 adults of age 18–90 years. Smoking status along with sociodemographic characteristics and measures of psychological distress using the Kessler 10-item questionnaire were collected using a face-to-face data collection method. Results The crude (age-standardized) prevalence of ever smoking was 27.1 (24.3)% that includes current 25.6 (23.7) and smoker 1.5 (0.6)%, and the prevalence of smokeless tobacco (SLT) was 23.5 (13.4)%. The prevalence of ever smoking was the highest in daily labourers (62.9%) and SLT use was the highest in widowed people (47.2%). After adjustment for covariates, no education (odds ratio (OR): 3.78, 95% confidence interval (CI): 1.57–9.07 for females and OR: 2.69, 95% CI: 1.87–3.87 for males) compared to at least secondary level of education and daily labours (OR: 6.66, 95% CI: 1.67–26.6 for females and OR: 5.12, 95% CI: 1.30–20.19 for males) compared to housework were associated with higher prevalence of ever smoking. Any level of psychological distress, such as mild psychological distress, was associated with at least double the prevalence of tobacco smoking in females (OR: 2.12, 95% CI: 1.67–3.83) but not in males (OR: 1.12, 95% CI: 0.80–1.56). Psychological distress was not associated with SLT use. Conclusions Prevalence of both smoking and SLT use was high, particularly in daily labourers, people with no education, and females with psychological distress in rural Bangladesh. Appropriate intervention programs should especially target those of low levels of education and laborious occupations for increasing awareness for the cessation of smoking in rural Bangladesh.
Occlusion resistant learning of intuitive physics from videos

To reach human performance on complex tasks, a key ability for artificial systems is to understand physical interactions between objects, and predict future outcomes of a situation. This ability, often referred to as intuitive physics, has recently received attention and several methods were proposed to learn these physical rules from video sequences. Yet, most of these methods are restricted to the case where no, or only limited, occlusions occur. In this work we propose a probabilistic formulation of learning intuitive physics in 3D scenes with significant inter-object occlusions. In our formulation, object positions are modeled as latent variables enabling the reconstruction of the scene. We then propose a series of approximations that make this problem tractable. Object proposals are linked across frames using a combination of a recurrent interaction network, modeling the physics in object space, and a compositional renderer, modeling the way in which objects project onto pixel space. We demonstrate significant improvements over state-of-the-art in the intuitive physics benchmark of IntPhys. We apply our method to a second dataset with increasing levels of occlusions, showing it realistically predicts segmentation masks up to 30 frames in the future. Finally, we also show results on predicting motion of objects in real videos.
The Thin Red Line-To What Extent can Crowd Workers Emulate the Performance of Experts?

Crowdsourcing has seen an increasing popularity for solving a variety of tasks. Specifically for tasks which address Web data interoperability, such as entity interlinking or schema mapping, crowdsourcing has been widely adopted in research and practice. In earlier work, we have investigated the behavioral pattern exhibited by workers, specifically, with respect to malicious activity and its detection. However, little research has been conducted so far to investigate the capability of crowd workers to substitute expert-based judgements, or, the extent to which worker performance is influenced by the task environment. In this work, we present results from a study which assesses the impact of the audience, crowd workers versus experts, and the specific task design and environment on the worker performance. Our study has been conducted on the specific tasks of link prediction and schema mapping, where tasks have been carried out by crowd workers and predefined experts. We compare the performance of crowd workers and experts in environments that differ with respect to the level of contextual information provided about the task at hand. Our results show that it is feasible to attain high quality results from crowd workers, that are comparable to experts when adequate context is presented to the crowd. In addition, we present a detailed analysis of the errors that the crowd and experts tend to commit in the task of link prediction and schema mapping. Finally, we present a novel method to assess the inherent difficulty of the link prediction task, and the impact of this task difficulty on the quality of the results.
Adolescent Violence Exposure and Victimization at Home: Coping and Psychological Trauma Symptoms*

The present study examined the relationship between home violence exposure and victimization and adolescent coping strategies, psychological trauma symptoms, and self-reported violent behavior. Data were collected from a sample of 3,734 adolescents in grades 9 through 12. Results indicated that both males and females reported high rates of home violence exposure and victimization. High levels of violence exposure at home were associated with adolescents utilizing more adaptive (e.g., going to church) and more maladaptive coping strategies (e.g., using alcohol or drugs) compared to adolescents in the low home violence exposure group. For both males and females, home violence exposure was also related to higher levels of psychological trauma symptoms including anger, depression, and post-traumatic stress. Average scores for violent behavior among adolescents in the high exposure group were more than three times higher for girls and two times higher for boys than scores for adolescents in the low exposure group. As expected, females endorsed more internalizing and interpersonal coping strategies, while males endorsed more instrumental coping strategies. This study illustrates the significant impact of home violence exposure and victimization on adolescents' ability to cope with significant stress, their experience of psychological trauma symptoms and their own violent behavior.
How do parental attributions affect child outcomes from positive parenting interventions? A mediation study

Problematic parental attributions refer to negative causal explanations for child problem behaviour and are known to predict parenting intervention outcomes. This study examines alternative accounts of how mothers’ problematic parental attributions, operationalised as negative pre-treatment and change resistant parental attributions during treatment, may affect child behaviour outcomes from a parenting intervention program. Putative mediators included parental feelings about the child and use of harsh discipline. Participants were 163 families with children aged from 3 to 16 referred to specialist clinics for the treatment of conduct problems. Measures were collected as part of pre-treatment, post-treatment, and 3-month follow-up assessments. Mothers’ pre-treatment and change resistant parental attributions were associated with smaller improvements in parental feelings at the end of treatment which in turn were associated with greater use of harsh discipline. Greater use of harsh discipline was associated with greater conduct problems overall. Smaller improvements in parental feelings mediated the effects of pre-treatment and change resistant parental attributions on outcomes in mothers’ use of harsh discipline and mediated the effects of change resistant parental attributions on outcomes in child conduct problems. Smaller improvements in parental feelings about the child may act as a mechanism that explains the impact of problematic parental attributions on treatment outcomes.
Misery perfusion syndrome at the chronic stage of cerebral infarction.

左中大脳動脈領域の脳梗塞に基づく失語症例64例を対象に,発症3日から2,048日まで,延べ93回の脳循環代謝測定を行い,脳血流量(CBF),脳酸素消費量(CMRO2),酸素摂取率(OEF),血管通過時間(VTT),脳血管反応性(VR)について解析した. 全体では18. 3%にOEFの上昇した貧困灌流症候群が観察され,7.5%は発症後30日から250日までの慢性期に出現した. 梗塞域では,OEFの上昇に加えて,VTTの延長,VRの低下が観察され,貧困灌流症候群を示す群ではOFFとVTTが相関していた. 対側半球や後頭葉の非梗塞域でも,CBFとCMRO2が低下し,OEFの上昇やVTTの延長もみられ,潜在的な低灌流状態に加えて脳梗塞の遠隔効果が病態を修飾している可能性が考えられた. アテローム血栓性脳塞栓と心原性脳梗塞の間で慢性期の脳循環代謝病態には明らかな差異は見られなかった.
Sharing Knowledge in Multi-Task Deep Reinforcement Learning

We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption that learning from different tasks, sharing common properties, is helpful to generalize the knowledge of them resulting in a more effective feature extraction compared to learning a single task. Intuitively, the resulting set of features offers performance benefits when used by Reinforcement Learning algorithms. We prove this by providing theoretical guarantees that highlight the conditions for which is convenient to share representations among tasks, extending the well-known finite-time bounds of Approximate Value-Iteration to the multi-task setting. In addition, we complement our analysis by proposing multi-task extensions of three Reinforcement Learning algorithms that we empirically evaluate on widely used Reinforcement Learning benchmarks showing significant improvements over the single-task counterparts in terms of sample efficiency and performance.
Effect of Educational Intervention about Work Related Musculoskeletal Disorders on Restaurant Workers in Toshiba Alarabi Factories at Benha City

Musculoskeletal disorders are the most common cause of physical disability in working individuals as restaurant work is physically demanding that develop symptoms of various musculoskeletal disorders. Occupational Health Nurse plays important role in improving or maintaining a worker’s health using prevention, protection and pro-active health interventions. The aim of the study was to evaluate the effect of an educational Intervention about work related musculoskeletal disorders on restaurant workers in Toshiba Alarabi Factories at Benha City. A quasi-experimental design was utilized for the study. The study was conducted at the restaurant of Toshiba Alarabi Factories at Benha city. A convenient sample of 43 workers was recruited for the study. Two tools were used for data collection; 1) a structured interviewing questionnaire to collect data about the workers' socio demographic characteristics, knowledge regarding work related musculoskeletal disorders and workers’ selfreported pain /discomfort in the last 12 months. 2) An observation sheet to assess workers 'practice during performing their tasks. The study results showed that 62.8 % of workers had more than10 years of experience and more than two thirds of them suffer from musculoskeletal discomfort with highly statistically significant differences (P<.001) before and after educational intervention regarding total knowledge and practices scores of the studied workers. Moreover, there was statistically significant correlation (P < 0.01) between the workers' total knowledge and practice scores before and after educational intervention. The study concluded that the implementation of the educational intervention significantly improved workers’ knowledge and practice regarding work related musculoskeletal disorders after the educational intervention than before intervention. The study recommended that adequately planned educational training programs must be conducted in order to improve workers’ knowledge and practices regarding prevention of work related musculoskeletal disorders.
Association of masked uncontrolled hypertension and cardiovascular diseases in treated hypertensive patients

Introduction The aim of the study was to evaluate the association of masked uncontrolled hypertension (MUCH) and prevalence of cardiovascular disease in treated hypertensive patients. Material and methods Patients’ demographics and prior medical histories were collected. Fasting venous blood was drawn for evaluation of serum creatinine level, which was used to calculate glomerular filtration rate (GFR). Clinic blood pressure (BP) and 24 h ambulatory blood pressure monitoring (ABPM) measurements were performed. Based on the clinic BP and 24 h ABPM results, patients were divided into MUCH and non-masked hypertension groups. Results Compared to patients without masked hypertension, MUCH patients were older (62.4 ±11.2 vs. 59.7 ±10.4 years, p < 0.05), more likely to be male (66.9% vs. 63.4%), had diabetes (33.9% vs. 29.6%), longer hypertension duration (12.4 ±5.3 vs. 9.5 ±4.5 years, p < 0.05), lower GFR (79.5 ±11.6 vs. 82.4 ±10.3 ml/min/1.73 m2, p < 0.05), treated with β-blocker (39.0% vs. 32.7%, p < 0.05) and required more antihypertensive medications (2.7 ±0.5 vs. 2.2 ±0.3, p < 0.05). MUCH patients have higher cardiovascular disease prevalence than that without masked hypertension (30.1% vs. 23.4%, p < 0.05). After adjustment for covariates, MUCH was still independently associated with higher cardiovascular disease prevalence with odds ratio 1.38 (95% confidence interval 1.17–1.62, p < 0.05). Conclusions The MUCH is independently associated with prevalent cardiovascular disease in treated hypertensive patients. Future studies are needed to evaluate whether correction of MUCH can improve patients’ outcomes.
SPROUT: Self-Progressing Robust Training

Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy and reliable machine learning systems. Current robust training methods such as adversarial training explicitly specify an “attack” (e.g., `∞-norm bounded perturbation) to generate adversarial examples during model training in order to improve adversarial robustness. In this paper, we take a different perspective and propose a new framework SPROUT, self-progressing robust training. During model training, SPROUT progressively adjusts training label distribution via our proposed parametrized label smoothing technique, making training free of attack generation and more scalable. We also motivate SPROUT using a general formulation based on vicinity risk minimization, which includes many robust training methods as special cases. Compared with stateof-the-art adversarial training methods (PGD-`∞ and TRADES) under `∞-norm bounded attacks and various invariance tests, SPROUT consistently attains superior performance and is more scalable to large neural networks. Our results shed new light on scalable, effective and attack-independent robust training methods.
PROM_A_229569 355..372

Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia Purpose: Patient-centred and value-based health-care organisations are increasingly recognising the importance of the patient perspective in the measurement and evaluation of health outcomes. This has been primarily implemented using patient-reported outcome measures (PROMs). Clinical quality registries (CQRs) are specifically designed to improve direct clinical care, benchmark health-care provision and inform health service planning and policy. Despite CQRs having incorporated the patient perspective to support the evaluation of health-care provision, no evidence-based guidelines for inclusion of PROMs in CQRs exist. This has led to substantial heterogeneity in capturing and reporting PROMs within this setting. This publication is the first in a series describing the development of evidenceinformed guidelines for PROMs inclusion within CQRs in Australia. Methods: This study consisted of three components: 1) a literature review of existing evidence of guidelines, enablers, barriers, and lessons learnt of PROMs use within the CQRs setting; 2) a survey of Australian CQRs to determine current practices for PROMs use and reporting; and 3) development of a preliminary conceptual framework for PROMs inclusion in CQRs. Results: Content analysis of the literature review and survey of 66 Australian registries elicited eight categories for the conceptual framework. The framework covers eight components: rationale, setting, ethics, selection of PROMs, administration, data management, statistical methods, feedback, and reporting. Conclusion: We developed a preliminary conceptual framework, which classified findings, from both the literature and the survey, into broad categories ranging from initial development to outcome dissemination providing the structure for development of guidelines in the next phase of this project, engaging national and international leaders in health-related quality of life research, clinicians, researchers, patient advocates and consumers.
PAC-Bayesian Neural Network Bounds

Bayesian neural networks, which both use the negative log-likelihood loss function and average their predictions using a learned posterior over the parameters, have been used successfully across many scientific fields, partly due to their ability to ‘effortlessly’ extract desired representations from many large-scale datasets. However, generalization bounds for this setting is still missing. In this paper, we present a new PAC-Bayesian generalization bound for the negative log-likelihood loss which utilizes the Herbst Argument for the log-Sobolev inequality to bound the moment generating function of the learners risk. We explore the generalization and calibration properties of the learned posterior on several image classification benchmarks, showing that the proposed approach provides better generalization and uncertainty estimates.
Can gradient clipping mitigate label noise?

Gradient clipping is a widely-used technique in the training of deep networks, and is generally motivated from an optimisation lens: informally, it controls the dynamics of iterates, thus enhancing the rate of convergence to a local minimum. This intuition has been made precise in a line of recent works, which show that suitable clipping can yield significantly faster convergence than vanilla gradient descent. In this paper, we propose a new lens for studying gradient clipping, namely, robustness: informally, one expects clipping to provide robustness to noise, since one does not overly trust any single sample. Surprisingly, we prove that for the common problem of label noise in classification, standard gradient clipping does not in general provide robustness. On the other hand, we show that a simple variant of gradient clipping is provably robust, and corresponds to suitably modifying the underlying loss function. This yields a simple, noise-robust alternative to the standard cross-entropy loss which performs well empirically.