Miracle-dz/UniFAR-FTUs huggingface dataset:
OP-HEPR180018 706..715

Despite the enormous potential of social media for health promotion, there is an inadequate evidence base for how they can be used effectively to influence behaviour. In Australia, research suggests social media use is higher among Aboriginal and Torres Strait Islander people than the general Australian population; however, health promoters need a better understanding of who uses technologies, how and why. This qualitative study investigates what types of health content are being shared among Aboriginal and Torres Strait people through social media networks, as well as how people engage with, and are influenced by, health-related information in their offline life. We present six social media user typologies together with an overview of health content that generated significant interaction. Content ranged from typical health-related issues such as mental health, diet, alcohol, smoking and exercise, through to a range of broader social determinants of health. Social media-based health promotion approaches that build on the social capital generated by supportive online environments may be more likely to generate greater traction than confronting and emotion-inducing approaches used in mass media campaigns for some health topics.
Crystal structure of 2-nitro-N′-(2-methoxynaphthylidene)- benzohydrazide, C19H15N3O4

C19H15N3O4, monoclinic, P121/c1 (no. 14), a = 10.909(4) Å, b = 11.084(4) Å, c = 14.888(6) Å, = 105.360(6)°, V = 1735.9 Å, Z = 4, Rgt(F) = 0.050, wRref(F) = 0.131, T = 298 K. Source of material 2-Methoxy-1-naphthylaldehyde and 2-nitrobenzohydrazide of analytical reagent (AR) grade were purchased from Aldrich and were used as obtained. 2-Methoxy-1-naphthylaldehyde (186.2 mg, 1.0 mmol) and 2-nitrobenzohydrazide (181.2 mg, 1.0 mmol) were dissolved in a methanol solution (50 ml). The mixture was stirred for 30 min at room temperature. The resulting solution was left in air for a few days, yielding colorless block-shaped crystals. Experimental details H1 atom was located in a difference map and refined with N—H distance restrained to 0.90(1) Å. The remaining H atoms were positioned geometrically [d(C—H) = 0.93 0.96 Å] and refined using a riding model, with Uiso(H) = 1.2 Ueq(C) and 1.5 Ueq(C19). A rotating group model was applied for the methyl group. Discussion Schiff bases are a kind of versatile compounds, which possess excellent biological properties [1-3]. Recently, a large number of Schiff bases derived from the reaction of aldehydes with benzohydrazides have been reported [4-6]. In the title crystal structure, all the bond lengths are comparable with those observed in other similar compounds [4-8]. The dihedral angle between the C1-C6 benzene ring and the C9-C18 naphthyl ring is 73.6(2)°. The dihedral angle between the O3/N3/O4 plane and the C1-C6 benzene ring is 17.0(2)°. The C19 atom deviates from the least-squares plane of C9-C18 by 0.245(2) Å. The torsion angles of C1$C7$N1$N2 and C7$N1$ N2$C8 are 3.6(2)° and 0.7(2)°, respectively. In the crystal structure, molecules are linked through intermolecular N$H···O hydrogen bonds to form dimers. Z. Kristallogr. NCS 224 (2009) 37-38 / DOI 10.1524/ncrs.2009. 0017 37 © by Oldenbourg Wissenschaftsverlag, München Crystal: colorless block, size 0.30 × 0.32 × 0.32 mm Wavelength: Mo K0 radiation (0.71073 Å) %: 0.96 cm Diffractometer, scan mode: Bruker SMART 1000 CCD, 2 2"max: 54° N(hkl)measured, N(hkl)unique: 14186, 3765 Criterion for Iobs, N(hkl)gt: Iobs > 2 ! (Iobs), 2288 N(param)refined: 239 Programs: SHELXS-97 [9], SHELXL-97 [10], SHELXTL [11], Table 1. Data collection and handling. H(3) 4e 0.8244 $0.0288 1.0956 0.073 H(4) 4e 0.8042 $0.0013 1.2442 0.090 H(5) 4e 0.8494 0.1821 1.3152 0.093 H(6) 4e 0.9151 0.3403 1.2396 0.076 H(8) 4e 0.6950 0.5032 0.8977 0.051 H(11) 4e 0.2951 0.4904 0.7085 0.080 H(12) 4e 0.1764 0.3923 0.7895 0.082 H(14) 4e 0.1561 0.2816 0.9267 0.082 H(15) 4e 0.2448 0.1995 1.0679 0.088 H(16) 4e 0.4627 0.2079 1.1309 0.079 H(17) 4e 0.5903 0.3004 1.0535 0.063 H(19A) 4e 0.4396 0.5419 0.6277 0.159 H(19B) 4e 0.5530 0.6330 0.6577 0.159 H(19C) 4e 0.4309 0.6506 0.6930 0.159 H(1) 4e 0.881(2) 0.482(1) 0.979(1) 0.080 Table 2. Atomic coordinates and displacement parameters (in Å). Atom Site x y z Uiso
SUPPLEMENTARY MATERIALS WITH THE LOCAL STORIES FOR EXTENSIVE READING ACTIVITY FOR THE NINTH GRADE OF SMP

Abstact This study aims at developing supplementary reading materials with local values of Sukadana for extensive reading activity designed for the ninth grades students of SMPN 1 Sukadana. Using research and development model adapted from Hyland (2003) and stages of materials development model adapted from Tomlinson (2012) as the bedrock of the study, supplementary reading materials containing four local stories is produced. The pictures and illustrations laid were able to assist the students in understanding the texts and also to attract them to read. The students are expected to use this product as additional materials for extensive reading activity since such materials are not available in their school library and in the market. In addition, teachers can implement Extensive Reading activity by utilizing this product since it can help the students to read more fluently and to expand their knowledge.
FACTORS AND THE PREVALENCE OF HYPERTENSION IN THE ADOLESCENT SCHOOL BOYS

Background: Hypertension is one of the diseases which is less commonly diagnosed and treated, but its actual prevalence is high. The association between the presence of risk factors and the development of disease has been well documented. The present study was conducted to find out the risk factors and to evaluate the prevalence of hypertension among the adolescent school boys of Ahmedabad city. Aims: 1.To study the distribution of blood pressure among the adolescent boys who were aged 12-19 years. 2. To find out the association between the prevalence of hypertension and it’s risk factors. Settings and Design: A cross sectional study was conducted from Feb. 2007 to August 2008 in secondary and higher secondary schools in 5 zones of the Ahmedabad Municipal Corporation. Methods and Material: A predesigned and pretested proforma was filled by the students after the purpose of the study was explained to them. Anthropometric measurements and blood pressure were recorded as per the standard WHO guidelines.1093 proformas were analyzed after excluding the incomplete proformas. Hypertension was diagnosed when the BP exceeded two standard deviations (i.e.95 percentile) above the mean pressure for the population. BMI was used as a measure of obesity and it was derived by using the standard formula for it. Statistical Analysis: The collected information was analyzed by using the Microsoft Excel and the Epi info 3.4 software. Chi-square test was used for analysis. Results and Conclusions: Out of 1093 adolescent boys, 107 (9.78 %) were found to be hypertensive. The highest prevalence was found at 19 years of age (21.7 %). Of the 107 hypertensive boys, 42 (39.2 %) had both systolic and diastolic hypertension. The mean SBP among the participants was 109.6 mm Hg and the mean DBP was 69.3 mmHg. The family history of HT and the presence of overweight and obesity in boys were found to be associated significantly. No association was found between hypertension and other risk factors like added salt, junk food and the socioeconomic class. Among the hypertensive adolescents, the risk factors which were found to have the highest prevalence were the intake of junk food (90.6%), followed by higher Khan M I, et al, Risk factors and Prevalence of Hypertension in Adolescent boys Journal of Clinical and Diagnostic Research. 2010 December;(4):3348-3354 3349 socio-economic class (48.5%), extra salt in the diet (29.9%) and overweight and obesity (20.3%).
EFFECTS OF PROBIOTIC FERMENTATION OF SELECTED MILK AND WHEY PROTEIN PREPARATIONS ON BIOACTIVE AND TECHNOLOGICAL PROPERTIES

The objective of this study was to compare the effect of probiotic fermentation (conducted by L. acidophilus LA-5) of milk containing an addition of selected whey or milk protein preparations (whey protein concentrates, whole milk, whey protein isolate, casein glycomacropeptide or α-lactalbumin) on the technological and functional properties of obtained milk beverages. Determination of the antioxidative activities and identification sequences of biologically active peptides generated in selected protein preparations during probiotic fermentation also constituted the aim of the research. The results obtained indicate that the addition (1%) of α-lactalbumin into regenerated skimmed milk in the highest degree reduced the syneresis level in fermented products. Also, αlactalbumin hydrolysates exhibited the strongest antioxidative properties (57.57±6.6%), while casein glycomacropeptide hydrolysates allowed us to obtain the highest amount of various biopeptides.
2020-15 Cooperation and Norm-Enforcement under Impartial vs. Competitive

We investigate, both theoretically and through a laboratory experiment, how diﬀerent forms of higher-order punishment aﬀect third party behavior and the level of cooperation in a public goods game. We compare two treatments where the third party is embedded in diﬀerent stylized institutions to a baseline treatment where this is not the case. In one treatment, the third party is evaluated by another uninvolved individual (“fourth party”); in the other, the third party faces competition by another potential third party punisher. We ﬁnd that third parties punish free-riders more severely if they have to fear negative payoﬀ consequences for themselves. Our results point to substantial qualitative diﬀerences between the institutional arrangements: When the third party is under scrutiny of a fourth party, punishment is high compared to the other treatments, while free-riding is at its lowest. By contrast, competition between two third party candidates leads to strategic and partial punishment. between turns rank sum tests with session averages yield Party Competition
Corner Case Data Description and Detection

As the major factors affecting the safety of deep learning models, corner cases and related detection are crucial in AI quality assurance for constructing safety- and security-critical systems. The generic corner case researches involve two interesting topics. One is to enhance DL models’ robustness to corner case data via the adjustment on parameters/structure. The other is to generate new corner cases for model retraining and improvement. However, the complex architecture and the huge amount of parameters make the robust adjustment of DL models not easy, meanwhile it is not possible to generate all real-world corner cases for DL training. Therefore, this paper proposes a simple and novel approach aiming at corner case data detection via a specific metric. This metric is developed on surprise adequacy (SA) which has advantages on capture data behaviors. Furthermore, targeting at characteristics of corner case data, three modifications on distanced-based SA are developed for classification applications in this paper. Consequently, through the experiment analysis on MNIST data and industrial data, the feasibility and usefulness of the proposed method on corner case data detection are verified.
Predicting CEO Compensation in Non-Controlled Public Corporations with the Canonical Regression Quantile Method

Empirical research on the relationship between CEO compensation and corporate performance has, as of yet, failed to yield meaningful results. Evidence instead points to the decoupling of CEO total compensation (salary, bonus, other payments, and stock options) from the corporate performance. Recent work by Barton et al (2017) suggested a predictive model where public corporations engaging in long-term operational and financial goals achieve superior performance and market results in the long-term. The suggested predictive power of this long-termism could provide a quantitative measure of future corporate performance that is tied to current CEO action. If true, this model can be used as a proxy to establish future CEO compensation. Corporate ownership structure can influence CEO compensation to reflect ownership powers rather than corporate performance. Therefore, we limited our research to non-controlled public corporations – those with a single class of shares and without blockholders. The analysis here uses Canonical Regression Quantiles to produce an Index based on past data in order to predict CEO compensation two years ahead (see Portnoy and Haimberg, 2020). The regressions on Index are significant, but not overwhelmingly so, but provide a useful tool to separate over-compensated from under-compensated CEOs, and are a method to determine current CEO compensation. Results: The use of the Canonical Regression Quantiles’ Index proved that non-controlled companies that invest in long-termism post superior future performance. The Index indicates that current CEO compensation influences future performance. The Index provides a method for determining CEO pay for the next 1-2 year and is a useful method to distinguish over/underpaid CEOs as an unbiased alternative to the peer groups comparison used by most compensation consultants. This determination is statistically weak, but future research using the Canonical Regression Quantiles with a larger data set may lead to increased sensitivity and a powerful unbiased method for replacing compensation consultants who are responsible for the decoupling of CEO compensation and corporate performance.
User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics

Topic models are widely used analysis techniques for clustering documents and surfacing thematic elements of text corpora. These models remain challenging to optimize and often require a “human-in-the-loop” approach where domain experts use their knowledge to steer and adjust. However, the fragility, incompleteness, and opacity of these models means even minor changes could induce large and potentially undesirable changes in resulting model. In this paper we conduct a simulation-based analysis of human-centered interactions with topic models, with the objective of measuring the sensitivity of topic models to common classes of user actions. We find that user interactions have impacts that differ in magnitude but often negatively affect the quality of the resulting modelling in a way that can be difficult for the user to evaluate. We suggest the incorporation of sensitivity and “multiverse” analyses to topic model interfaces to surface and overcome these deficiencies. Code and Data Availability: https://osf.io/zgqaw
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization

We extend the Approximate-Proximal Point (aProx) family of model-based methods for solving stochastic convex optimization problems, including stochastic subgradient, proximal point, and bundle methods, to the minibatch and accelerated setting. To do so, we propose specific model-based algorithms and an acceleration scheme for which we provide non-asymptotic convergence guarantees, which are order-optimal in all problem-dependent constants and provide linear speedup in minibatch size, while maintaining the desirable robustness traits (e.g. to stepsize) of the aProx family. Additionally, we show improved convergence rates and matching lower bounds identifying new fundamental constants for"interpolation"problems, whose importance in statistical machine learning is growing; this, for example, gives a parallelization strategy for alternating projections. We corroborate our theoretical results with empirical testing to demonstrate the gains accurate modeling, acceleration, and minibatching provide.
XAD-2 HPTLC Method of Identification and Determination of Some Synthetic Food Colourings

Abstract An HPTLC method for identification and determination of 12 synthetic dyes (Amaranth, Allura red, Brilliant black BN, Brilliant blue E, Carmoisine, Erythrosine, Indigotine, Patent blue V, Ponceau 4R, Quinoline yellow, Sunset yellow, Tartazine) has been developed. Amberlite XAD-2 was used as a stationary phase for column chromatography. Elution was performed with various acidified alcohols (i-propanol, n-propanol, n-butanol) and recoveries between 81.5 - 100.2% were obtained. The low limit of detection is in the range of 4 – 10 ng. The method was tested on commercial products as: carbonated soda, candies, chewing gum, identical natural flavours, etc.
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks

It is desirable, and often a necessity, for machine learning models to be robust against adversarial attacks. This is particularly true for Bayesian models, as they are well-suited for safety-critical applications, in which adversarial attacks can have catastrophic outcomes. In this work, we take a deeper look at the adversarial robustness of Bayesian Neural Networks (BNNs). In particular, we consider whether the adversarial robustness of a BNN can be increased by model choices, particularly the Lipschitz continuity induced by the prior. Conducting in-depth analysis on the case of i.i. d. , zero-mean Gaussian priors and posteriors approximated via mean-field variational inference, we find evidence that adversarial robustness is indeed sensitive to the prior variance.
An optical observational cluster mass function at z ∼ 1 with the ORELSE survey

We present a new mass function of galaxy clusters and groups using optical/near-infrared (NIR) wavelength spectroscopic and photometric data from the Observations of Redshift Evolution in Large-Scale Environments (ORELSE) survey. At z ∼ 1, cluster mass function studies are rare regardless of wavelength and have never been attempted from an optical/NIR perspective. This work serves as a proof of concept that z ∼ 1 cluster mass functions are achievable without supplemental X-ray or Sunyaev-Zel’dovich data. Measurements of the cluster mass function provide important contraints on cosmological parameters and are complementary to other probes. With ORELSE, a new cluster finding technique based on Voronoi tessellation Monte Carlo (VMC) mapping, and rigorous purity and completeness testing, we have obtained ∼240 galaxy overdensity candidates in the redshift range 0.55 < z < 1.37 at a mass range of 13.6 < log (M/M⊙) < 14.8. This mass range is comparable to existing optical cluster mass function studies for the local universe. Our candidate numbers vary based on the choice of multiple input parameters related to detection and characterization in our cluster finding algorithm, which we incorporated into the mass function analysis through a Monte Carlo scheme. We find cosmological constraints on the matter density, Ωm, and the amplitude of fluctuations, σ8, of $\Omega _{m} = 0.250^{+0.104}_{-0.099}$ and $\sigma _{8} = 1.150^{+0.260}_{-0.163}$. While our Ωm value is close to concordance, our σ8 value is ∼2σ higher because of the inflated observed number densities compared to theoretical mass function models owing to how our survey targeted overdense regions. With Euclid and several other large, unbiased optical surveys on the horizon, VMC mapping will enable optical/NIR cluster cosmology at redshifts much higher than what has been possible before.
Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification

We reconsider the evaluation of OOD detection methods for image recognition. Although many studies have been conducted so far to build better OOD detection methods, most of them follow Hendrycks and Gimpel's work for the method of experimental evaluation. While the unified evaluation method is necessary for a fair comparison, there is a question of if its choice of tasks and datasets reflect real-world applications and if the evaluation results can generalize to other OOD detection application scenarios. In this paper, we experimentally evaluate the performance of representative OOD detection methods for three scenarios, i.e., irrelevant input detection, novel class detection, and domain shift detection, on various datasets and classification tasks. The results show that differences in scenarios and datasets alter the relative performance among the methods. Our results can also be used as a guide for practitioners for the selection of OOD detection methods.
GraphHop: An Enhanced Label Propagation Method for Node Classification

A scalable semisupervised node classification method on graph-structured data, called GraphHop, is proposed in this work. The graph contains all nodes’ attributes and link connections but labels of only a subset of nodes. Graph convolutional networks (GCNs) have provided superior performance in node label classification over the traditional label propagation (LP) methods for this problem. Nevertheless, current GCN algorithms suffer from a considerable amount of labels for training because of high model complexity or cannot be easily generalized to large-scale graphs due to the expensive cost of loading the entire graph and node embeddings. Besides, nonlinearity makes the optimization process a mystery. To this end, an enhanced LP method, called GraphHop, is proposed to tackle these problems. GraphHop can be viewed as a smoothening LP algorithm, in which each propagation alternates between two steps: label aggregation and label update. In the label aggregation step, multihop neighbor embeddings are aggregated to the center node. In the label update step, new embeddings are learned and predicted for each node based on aggregated results from the previous step. The two-step iteration improves the graph signal smoothening capacity. Furthermore, to encode attributes, links, and labels on graphs effectively under one framework, we adopt a two-stage training process, i.e., the initialization stage and the iteration stage. Thus, the smooth attribute information extracted from the initialization stage is consistently imposed in the propagation process in the iteration stage. Experimental results show that GraphHop outperforms state-of-the-art graph learning methods on a wide range of tasks in graphs of various sizes (e.g., multilabel and multiclass classification on citation networks, social graphs, and commodity consumption graphs).
Coherent perfect absorption of quantum light

Coherent perfect absorption (CPA) was introduced as a classical optics phenomenon of a standing wave absorption by a subwavelength film. In this paper, we develop a theory of CPA of quantized standing waves taking account of a subwavelength thickness of the absorber. This approach allows us to merge all known quantum effects of CPA under a single theory and introduce new regimes of CPA including CPA of NOON states with arbitrary number of entangled photons, orthogonally squeezed vacuum states, continuous variable entangled states and Schrödinger cat states. Detailed analysis of quantum regime of CPA sheds light on fundamental aspects of quantum light dissipation and its practical implementation in quantum optics and quantum information.
CINDy: Conditional gradient-based Identification of Non-linear Dynamics -- Noise-robust recovery

Governing equations are essential to the study of nonlinear dynamics, often enabling the prediction of previously unseen behaviors as well as the inclusion into control strategies. The discovery of governing equations from data thus has the potential to transform data-rich fields where well-established dynamical models remain unknown. This work contributes to the recent trend in data-driven sparse identification of nonlinear dynamics of finding the best sparse fit to observational data in a large library of potential nonlinear models. We propose an efficient first-order Conditional Gradient algorithm for solving the underlying optimization problem. In comparison to the most prominent alternative algorithms, the new algorithm shows significantly improved performance on several essential issues like sparsity-induction, structure-preservation, noise robustness, and sample efficiency. We demonstrate these advantages on several dynamics from the field of synchronization, particle dynamics, and enzyme chemistry.