Miracle-dz/UniFAR-FTUs huggingface dataset:
Phyllanthin from the plant Phyllanthus amarus

Department of Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600 025, India, Department of Microbiology, ALM Post Graduate Institute of Basic Medical Sciences, University of Madras, Taramani, Chennai 600 013, India, Department of Pharmacognacy, Government College of Pharmacy, Bangalore 560 027, India, and X-ray Crystallography Unit, School of Physics, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
PRBM_A_240898 223..232

Institute of Psychology, University of Szczecin, Szczecin 71-017, Poland Background: The functioning of the family of origin seems to be one of the key variables that contribute to life satisfaction. Since relationships with one’s parents are associated with well-being throughout life, the purpose of our study was to examine the association between family functioning and life satisfaction among Polish adults. Moreover, because some researchers postulate that family functioning affects quality of life directly as well as indirectly through some other variables, we focused on investigating how emotional intelligence might affect the link between family functioning and life satisfaction, as the character of this relationship has received surprisingly little attention. Patients, Methods and Data Collection: The sample consisted of 204 participants (86% women). We measured family functioning, satisfaction with life, and emotional intelligence. The data were collected using online forums through convenience sampling on the basis of availability and the willingness of the participants to respond. Results: The results showed that both life satisfaction and emotional intelligence correlated positively and significantly with cohesion, flexibility, communication, and family satisfaction. Life satisfaction correlated negatively and significantly with enmeshed, disengaged, and chaotic functioning. In contrast, emotional intelligence correlated negatively and significantly only with chaotic and disengaged functioning. Moreover, emotional intelligence partially mediated the relationship between six dimensions of family functioning (cohesion, flexibility, communication, family satisfaction, disengagement, and chaos) and life satisfaction. Conclusion: Our findings provide evidence of an indirect association between family functioning and life satisfaction through the mediating role of emotional intelligence. They indicate that individuals who evaluate their family functioning as cohesive, flexible, communicative, and fulfilled, are more likely to process their own emotions and enjoy higher life satisfaction. Conversely, assessment of family of origin as disengaged and chaotic may diminish the ability to manage one’s own emotions, which, in turn, can lead to lower life satisfaction.
Are competitors with high heels more likely to win the business marathon in the new millennium ?

Women are supposed to be in the kitchen and take care of the children while men earn the money and go “job-hunting”. ’ – Statements like this have been common in a lot of countries, cultures and families for many decades now. However, the world is constantly changing and so are the circumstances the two sexes live in. During the last 20 to 30 years there has been a drastic change concerning the newly opened possibilities for females to enter leadership positions. The questions that come to our mind immediately when hearing those readings are such as: • What factors does a certain leadership style depend on? • Could there also be other reasons beside the gender aspect why humans react in a certain way? • What attributes and characteristics are required from today’s leaders? Although there are many more questions that can be asked regarding this topic, the ones mentioned above, which make up the research questions of this study, seem to be the most important ones. Following an inductive research process, empirical material was gathered through questionnaires and face-to-face interviews. This collected information and the shared experiences are the representative data in this particular thesis from male and female leaders in different companies/organizations and various environments concentrating on our two home countries Sweden and Germany. Supported by a theoretical framework which consists of theories, books and articles of many well-known authors, especially the studies by the great German philosopher Immanuel Kant (1724-1804), the reader will get a wider knowledge of the empirical data. A separate chapter about the depending leadership styles and the ‘condition-theory’ by Immanuel Kant will help to explain certain behaviours by male and female leaders as well as describe the reasons why our society sees the leaders’ characteristics the way it does. The conditions that are relevant in this thesis are about the attitudes, soft skills such as communication aspects, attributes being used to describe daily situations as well as personal features of current and future leaders in prospect of career chances and risks. The following thesis aims for a clearer picture and greater awareness of particular settings and should be beneficial for current and future leaders from both sexes in all kinds of environments as well as people who work together with leaders frequently to gain a better understanding of their behaviours and decisions. Last, but not least there will be a part with a general review as well as personal reflections upon this topic and suggestions for further and future research areas besides the analysis of the whole thesis work. Keyword(s) Leadership, Management, Gender issues, Conditions, Globalization, Human behaviour, Communication
AGE AND GENDER’S INTERACTIVE EFFECTS ON ADULT LEARNERS’ HELP-SEEKING BEHAVIOR

Despite intentions to promote lifelong learning in Taiwan, there are nevertheless many limitations and challenges to doing so. In constructing a lifelong learning society wherein individuals are guaranteed a right to learn, mechanisms must be established to ensure that such opportunities are accessible to all citizens. Hence, the promotion of lifelong learning in Taiwan requires extensive planning and policy intervention in conjunction with the provisioning of high-quality information and counseling services to ensure that an awareness of adult learning opportunities is achieved. Research has generally focused on the eff ect of gender and age on helpseeking behaviors among adult learners. Nevertheless, little attention has been paid to the interactive eff ects of age and gender diff erences on the help-seeking behaviors of adult learners. In this study, a total of 785 participants were enrolled in the Division of Continuing and Extension Education from southern Taiwan. The data collected were subsequently examined using descriptive statistics and multivariate analysis of variance. An interactive eff ect was identifi ed between gender and age in relation to instrumental help-seeking and avoiding help-seeking. Furthermore, 21–39 years-old adult learners diff ered by gender in the form of help-seeking behavior. In adult learners from diff erent age groups, instrumental help-seeking and avoiding help-seeking behaviors existed in diff erent cycles by gender. Based on this study, suggestions for teachers, administrators, and future study were also provided.
Functional norm regularization for margin-based ranking on temporal data

Quantifying the properties of interest is an important problem in many domains, e.g., assessing the condition of a patient, estimating the risk of an investment or relevance of the search result. However, the properties of interest are often latent and hard to assess directly, making it difficult to obtain classification or regression labels, which are needed to learn a predictive models from observable features. In such cases, it is typically much easier to obtain relative comparison of two instances, i.e. to assess which one is more intense (with respect to the property of interest). One framework able to learn from such kind of supervised information is ranking SVM, and it will make a basis of our approach. Applications in bio-medical datasets typically have specific additional challenges. First, and the major one, is the limited amount of data examples, due to an expensive measuring technology, and/or infrequency of conditions of interest. Such limited number of examples makes both identification of patterns/models and their validation less useful and reliable. Repeated samples from the same subject are collected on multiple occasions over time, which breaks IID sample assumption and introduces dependency structure that needs to be taken into account more appropriately. Also, feature vectors are highdimensional, and typically of much higher cardinality than the number of samples, making models less useful and their learning less efficient. Hypothesis of this dissertation is that use of the functional norm regularization can help alleviating mentioned challenges, by improving generalization abilities and/or learning efficiency of predictive models, in this case specifically of the approaches based on the ranking SVM framework. The temporal nature of data was addressed with loss that fosters temporal smoothness of functional mapping, thus accounting for assumption that temporally
Coordination of Multiple Distributed Energy Resources to Regulate the Voltage using Adaptive Voltage Control Method

An adaptive voltage control method has been proposed to dynamically modify the control parameters of a single Distributed energy (DE) to respond to system changes such that the ideal response can be achieved. Theoretical analysis shows that a corresponding formulation of the dynamic control parameters exists; hence, the adaptive control method is theoretically solid. Then, control methods have been discussed in the case of multiple Distributed energy resources regulating voltages considering the availability of communications among all the Distributed energy resources. When communications are readily available, a method is proposed to directly calculate the needed adaptive change of the Distributed energy resource control parameters in order to achieve the ideal response. When there is no communication available, an approach to adaptively and incrementally adjust the control parameters based on the local voltage changes is proposed. Since the impact from other Distributed energy resources is implicitly considered in this approach, multiple Distributed energy resources can collectively regulate voltages closely following the ideal response curve.
Physical exercise versus natural estrogen supplement in treatment of metabolic syndrome in aged postmenopausal Wistar rats

The present study was performed to evaluate the effects of either a low intensity swimexercise program or low soybean flour supplementation on the different components of metabolic syndrome in aged postmenopausal rats. Fifty five rats, weighing 260-350 gm, were randomly allocated into: Control group (C), Metabolic Syndrome group(MS), Metabolic syndrome 6-week swim exercised group (MSE), and Metabolic syndrome 6 week -soybean flour supplemented group (MSS). All rats were followed weekly for assessment of body weight and arterial blood pressure which was found to be significantly increased in all metabolic syndrome groups (MS,MSE and MSS) compared to C group. At the end of the experimental period, all rats were subjected to measurement of arterial blood pressure. Biochemical analysis including plasma insulin and fibrinogen levels, and lipid profile was performed, then HOMA-IR and atherogenic index (AI) were calculated. MS rats exhibited significant increase in their body weight gain (BWG), visceral fat (VF), visceral fat/ body weight ratio (VF/BW Ratio), arterial blood pressure values (ABP), as well as, plasma triglyceride level (TG) and AI compared to their controls. MSE group exhibited significant decrease in their BWG, VF, VF/BW Ratio and AI compared to MS group. The MSS had significantly decreased BMI, VF and VF/BW Ratio, ABP values, plasma TG and AI compared to MS group. Plasma levels of insulin and total cholesterol were significantly higher in MSS group compared to the MS group. MSE group showed normalization of the values of their BWG, VF, VF/BW Ratio. MSS group showed normalization of their BMI, VF and VF/BW Ratio. When the (MSS) group was compared to the MSE group, a significant increase in plasma insulin level was observed in addition to significant decrease in ABP values. Conclusion: Low dietary intake of soybean flour or low intensity swimexercise program for 6 weeks were equally effective in normalizing visceral adiposity, but were partially effective as regards hypertriglyceridemia and hypertension in metabolic syndrome aged rats.
Prescribing pattern and cost analysis of antihypertensives in India

Background: Hypertension has been reported to be the strongest modifiable global risk factor for cardiovascular morbidity, mortality as well as health burdens. Antihypertensive pharmacotherapy effectively reduces hypertension-related morbidity and mortality. Prescribing pattern surveys are one of the drug use evaluation techniques providing an unbiased picture and identification of suboptimal prescribing patterns. Objective: The 6-month cross-sectional study was designed to assess the prescription pattern and cost of antihypertensives therapy in a health-care resource-limited setting of India. Materials and Methods: The hypertensive patients were divided into two groups according to risk assessment using the World Health Organization and International Society of Hypertension guidelines for the management of hypertension. The average drug acquisition and the percentage expenditure costs were calculated for each drug class on a daily and annual basis, and InStat GraphPad Prism was the statistical tool used. Results: In our study of 100 patients, the most commonly prescribed antihypertensives were calcium channel blockers in 49.81% and beta-blockers in 12.73% patients, respectively. The cost analysis on antihypertensive medications utilized showed a total expenditure of Rs. 3823.58 invested in 1 year. Conclusion: The drug use pattern of antihypertensives was evidenced based but imposed economic burden in patients. Hence, rational use of generic medications was recommended.
Removing Disparate Impact of Differentially Private Stochastic Gradient Descent on Model Accuracy

When we enforce differential privacy in machine learning, the utility-privacy trade-off is different w.r. t. each group. Gradient clipping and random noise addition disproportionately affect underrepresented and complex classes and subgroups, which results in inequality in utility loss. In this work, we analyze the inequality in utility loss by differential privacy and propose a modified differentially private stochastic gradient descent (DPSGD), called DPSGD-F, to remove the potential disparate impact of differential privacy on the protected group. DPSGD-F adjusts the contribution of samples in a group depending on the group clipping bias such that differential privacy has no disparate impact on group utility. Our experimental evaluation shows how group sample size and group clipping bias affect the impact of differential privacy in DPSGD, and how adaptive clipping for each group helps to mitigate the disparate impact caused by differential privacy in DPSGD-F.
Improving sampling and calibration of GRBs as distance indicators

We present a sample of 74 Gamma-Ray Bursts (GRBs) from the Fermi-GBM catalogue for which we compute the distance moduli and use them to constrain effective dark energy models. To overcome the circularity problem affecting GRBs as distance indicators, we calibrate the Amati relation of our sample with a cosmology-independent technique. Specifically, we use the latest observational Hubble parameter data, including associated systematics, to approximate the cosmic expansion through a Bezier parametric curve. We subsequently obtain the distance moduli of the GRBs and include the data in a suite of recent cosmological observations of the expansion history (Planck Compressed 2018, 2012 BOSS release of BAO data and Pantheon SNIa), to compute Bayesian posterior constraints for the standard cosmological model ΛCDM, as well as ωCDM, and the CPL parametrization. Throughout the analysis we strive to keep under control the error propagation and limit our GRBs sample to avoid observational bias. As a result, we find no evidence in favour of the alternatives to ΛCDM model. The latter agrees very well with our calibrated sample of GRBs and presently available luminosity distance probes.
Dynamic Backdoor Attacks Against Machine Learning Models

Machine learning (ML) has made tremendous progress during the past decade and is being adopted in various critical real-world applications. However, recent research has shown that ML models are vulnerable to multiple security and privacy attacks. In particular, backdoor attacks against ML models have recently raised a lot of awareness. A successful backdoor attack can cause severe consequences, such as allowing an adversary to bypass critical authentication systems. Current backdooring techniques rely on adding static triggers (with fixed patterns and locations) on ML model inputs which are prone to detection by the current backdoor detection mechanisms. In this paper, we propose the first class of dynamic backdooring techniques against deep neural networks (DNN), namely Random Backdoor, Backdoor Generating Network (BaN), and conditional Backdoor Generating Network (c-BaN). Triggers generated by our techniques can have random patterns and locations, which reduce the efficacy of the current backdoor detection mechanisms. In particular, BaN and c-BaN based on a novel generative network are the first two schemes that algorithmically generate triggers. Moreover, c-BaN is the first conditional backdooring technique that given a target label, it can generate a target-specific trigger. Both BaN and c-BaN are essentially a general framework which renders the adversary the flexibility for further customizing backdoor attacks. We extensively evaluate our techniques on three benchmark datasets: MNIST, CelebA, and CIFAR-10. Our techniques achieve almost perfect attack performance on back-doored data with a negligible utility loss. We further show that our techniques can bypass current state-of-the-art defense mechanisms against backdoor attacks, including ABS, Februus, MNTD, Neural Cleanse, and STRIP.
Proximal bundle algorithms for nonsmooth convex optimization via fast gradient smooth methods

We propose new proximal bundle algorithms for minimizing a nonsmooth convex function. These algorithms are derived from the application of Nesterov fast gradient methods for smooth convex minimization to the so-called Moreau-Yosida regularization $F_\mu$ of $f$ w.r. t. some $\mu>0$. Since the exact values and gradients of $F_\mu$ are difficult to evaluate, we use approximate proximal points thanks to a bundle strategy to get implementable algorithms. One of these algorithms appears as an implementable version of a special case of inertial proximal algorithm. We give their complexity estimates in terms of the original function values, and report some preliminary numerical results.
Outdoor mmWave Base Station Placement: A Multi-Armed Bandit Learning Approach.

Base station (BS) placement in mobile networks is critical to the efficient use of resources in any communication system and one of the main factors that determines the quality of communication. Although there is ample literature on the optimum placement of BSs for sub-6 GHz bands, channel propagation characteristics, such as penetration loss, are notably different in millimeter-wave (mmWave) bands than in sub-6 GHz bands. Therefore, designated solutions are needed for mmWave systems to have reliable quality of service (QoS) assessment. This article proposes a multi-armed bandit (MAB) learning approach for the mmWave BS placement problem. The proposed solution performs viewshed analysis to identify the areas that are visible to a given BS location by considering the 3D geometry of the outdoor environments. Coverage probability, which is used as the QoS metric, is calculated using the appropriate path loss model depending on the viewshed analysis and a probabilistic blockage model and then fed to the MAB learning mechanism. The optimum BS location is then determined based on the expected reward that the candidate locations attain at the end of the training process. Unlike the optimization-based techniques, this method can capture the time-varying behavior of the channel and find the optimal BS locations that maximize long-term performance.
General-Purpose Communicative Function Recognition using a Hierarchical Network with Cascading Outputs and Maximum a Posteriori Path Estimation

ISO 24617-2, the standard for dialog act annotation, defines a hierarchically organized set of general-purpose communicative functions. The automatic recognition of these functions, although practically unexplored, is relevant for a dialog system, since they provide cues regarding the intention behind the segments and how they should be interpreted. In this paper, we explore the recognition of general-purpose communicative functions in the DialogBank, which is a reference set of dialogs annotated according to the standard. To do so, we adapt a state-of-the-art approach on flat dialog act recognition to deal with the hierarchical classification problem. More specifically, we propose the use of a hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Furthermore, since the amount of dialogs in the DialogBank is reduced, we rely both on additional dialogs annotated using mapping processes and on transfer learning to improve performance. The results of our experiments show that the hierarchical approach outperforms a flat one and that maximum a posteriori estimation outperforms an iterative prediction approach based on masking.
STD-Net: Structure-preserving and Topology-adaptive Deformation Network for 3D Reconstruction from a Single Image

3D reconstruction from a single view image is a long-standing prob-lem in computer vision. Various methods based on different shape representations(such as point cloud or volumetric representations) have been proposed. However,the 3D shape reconstruction with fine details and complex structures are still chal-lenging and have not yet be solved. Thanks to the recent advance of the deepshape representations, it becomes promising to learn the structure and detail rep-resentation using deep neural networks. In this paper, we propose a novel methodcalled STD-Net to reconstruct the 3D models utilizing the mesh representationthat is well suitable for characterizing complex structure and geometry this http URL reconstruct complex 3D mesh models with fine details, our method consists of(1) an auto-encoder network for recovering the structure of an object with bound-ing box representation from a single image, (2) a topology-adaptive graph CNNfor updating vertex position for meshes of complex topology, and (3) an unifiedmesh deformation block that deforms the structural boxes into structure-awaremeshed models. Experimental results on the images from ShapeNet show that ourproposed STD-Net has better performance than other state-of-the-art methods onreconstructing 3D objects with complex structures and fine geometric details.