Miracle-dz/UniFAR-FTUs huggingface dataset:
Closed-End Fund Discounts and Taxes*

We revisit the effect of personal taxes on closed-end fund discounts. After documenting CRSP errors in classifying the tax status of distributions, we make use of two important institutional features that the related literature has ignored: (i) tax rate changes, and (ii) non-taxable distributions. We find that taxable distributions lead to discount reductions, consistent with a taxbased explanation of the discount. Moreover, the discount reduction following a taxable distribution is greater during high tax periods than during low tax periods. In contrast, non-taxable distributions have no discernible impact on the discount, again consistent with a tax-based explanation of the discount. Importantly, we are able to rule out a competing short-term trading hypothesis. Taken together, our results highlight the importance of personal taxes in explaining closed-end fund discounts. JEL Classification: G10, G23, H20
Red Dragon AI at TextGraphs 2019 Shared Task: Language Model Assisted Explanation Generation

The TextGraphs-13 Shared Task on Explanation Regeneration (Jansen and Ustalov, 2019) asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI’s entries used the language of the questions and explanation text directly, rather than a constructing a separate graph-like representation. Our leaderboard submission placed us 3rd in the competition, but we present here three methods of increasing sophistication, each of which scored successively higher on the test set after the competition close.
EVALUATION OF INTERNET CORPORATION OF ASSIGNED NAMES AND NUMBERS (ICANN) COMPLIANCE PROCESSES

This document is a detailed study of the Compliance process at the Internet Corporation of Assigned Names and Numbers (ICANN), specifically the way it handles and tracks complaints of inaccurate WHOIS records for registered Generic Top Level Domains (gTLDs). ICANN issues accreditations to companies called Registrars which in turn are authorized to sponsor domain names (e.g., amazon.com) for companies and consumers. The domain owners, called registrants, must comply with agreements with the sponsoring Registrar and with ICANN including the maintenance of accurate WHOIS data. Registrars are obligated follow certain procedures, in the event of a complaint about WHOIS inaccuracy. ICANN is required to track those complaints to ensure proper handling. The results of this examination reveal a number of failures and inconsistencies within the ICANN Compliance process as well as apparent confusion over actual policy. Compliance staff also issued contradictory answers to questions at different times. Specific non-compliant events were not followed by enforcement for unexplained reasons. Compliance staff appeared to be unable to account for their decision-making process. At the end of this review Compliance appeared to abandon the investigations and refused to answer further questions.
EmpGAN: Multi-resolution Interactive Empathetic Dialogue Generation

Conventional emotional dialogue system focuses on generating emotion-rich replies. Studies on emotional intelligence suggest that constructing a more empathetic dialogue system, which is sensitive to the users' expressed emotion, is a crucial step towards a more humanized human-machine conversation. However, obstacles to establishing such an empathetic conversational system are still far beyond current progress: 1) Simply considering the sentence-level emotions while neglecting the more precise token-level emotions may lead to insufficient emotion perceptivity. 2) Merely relying on the dialogue history but overlooking the potential of user feedback for the generated responses further aggravates the insufficient emotion perceptivity deficiencies. To address the above challenges, we propose the EmpGAN, a multi-resolution adversarial empathetic dialogue generation model to generate more appropriate and empathetic responses. To capture the nuances of user feelings sufficiently, EmpGAN generates responses by jointly taking both the coarse-grained sentence-level and fine-grained token-level emotions into account. Moreover, an interactive adversarial learning framework is introduced to further identify whether the generated responses evoke emotion perceptivity in dialogues regarding both the dialogue history and user feedback. Experiments show that our model outperforms the state-of-the-art baseline by a significant margin in terms of both content quality as well as the emotion perceptivity. In particular, the distinctiveness on the DailyDialog dataset is increased up to 129%.
Short-Term Pre-Pregnancy Health Education May Improve Maternal Nutrition Behavior, but not Nutrition Self-Efficacy

Dr. Bastani has presented an interesting randomizedcontrolled trial on the effect of nutritional education on behavioral intention and self-efficacy in women with focus on iron and folic acid intakes in the J Health Scope (1). Iron and folic acid deficiencies during gestation are often associated with poor pregnancy outcomes for both mother and fetus (2). Although pre-conception counseling and nutrition education could be an ideal opportunity to improve maternal daily iron and folic acid intakes before conception, implementation of this form of preventive intervention is still lacking in many non-Western countries, particularly in Iran (3, 4). The one-day nutrition workshop, involving 8-12 women from the experimental group (n = 50) in each workshop, was preceded by an individualized advise session on healthy lifestyle and benefits of healthy diet. The control group received standard care (n = 49). The intervention lead to increased behavioral intention on iron and folic acid intakes among women planning pregnancy, but did not improve self-efficacy (1). There appears to be confusion on the concept of behavioral intention, self-efficacy and attitudes. It is therefore important to have a clear idea of some definitions before interpreting the findings. Behavioral intention measures a person’s relative strength of intention to perform a behavior. Attitude consists of individual’s feeling (positive/ negative) about the consequences of performing the behavior. Self-efficacy is a person’s belief in his/her own competence of enacting a certain behavior to attain a set of goals (5). The findings suggest that in order to improve levels of self-efficacy towards dietary changes with pre-conception women, more intense efforts are likely needed. Providing information on the benefits of healthy dietary habits and raising awareness of problems associated with unhealthy dietary habits may increase the knowledge, but not the willingness and ability to adopt healthy lifestyle. Socio-economic factors, social norms, family attitudes, and fear of failure can be important barriers to change. The current intervention might have benefited from a booster session providing reassurance and constructive feedback. Future intervention may consider
Astrochemistry as a Tool To Follow Protostellar Evolution: The Class I Stage

The latest developments in astrochemistry have shown how some molecular species can be used as a tool to study the early stages of the solar-type star formation process. Among them, the more relevant species are the interstellar complex organic molecules (iCOMs) and the deuterated molecules. Their analysis give us information on the present and past history of protostellar objects. Among the protostellar evolutionary stages, Class I protostars represent a perfect laboratory in which to study the initial conditions for the planet formation process. Indeed, from a physical point of view, the Class I stage is the bridge between the Class 0 phase, dominated by the accretion process, and the protoplanetary disk phase, when planets form. Despite their importance, few observations of Class I protostars exist and very little is known about their chemical content. In this paper we review the (few) existing observations of iCOMs and deuterated species in Class I protostars. In addition, we present new observations of deuterated cyanoacetylene and thioformaldehyde towards the Class I protostar SVS13-A. These new observations allow us to better understand the physical and chemical structure of SVS13-A and compare the cyanoacetylene and thioformaldehyde deuteration with other sources in different evolutionary phases.
Goos–Hänchen and Imbert–Fedorov shifts for epsilon-near-zero materials

We investigate the reflection of a Gaussian beam impinging upon the surface of an epsilon-near-zero (ENZ) medium. In particular, we discuss the occurrence of Goos–Hänchen and Imbert–Fedorov shifts. Our calculations reveal that spatial shifts are significantly enhanced owing to the ENZ nature of the medium, and that their value and angular position can be tuned by tuning the plasma frequency of the medium.
Empirical model of campus air temperature and urban morphology parameters based on field measurement and machine learning in Singapore

The rising air temperature caused by Urban Heat Island (UHI) effect has become a problem for Singapore, it not only affects the thermal comfort of outdoor microclimate environment, but also increases the cooling energy consumption of buildings. As part of a multiscale and multi-physics urban microclimate model, weather stations were installed at 15 points within kent ridge campus of National University of Singapore (NUS) and continuously recorded the microclimate data from February 2019 to May 2019. A Geographical Information System (GIS) map and 3D model were constructed for extracting urban morphology parameters such as BDG, PAVE, WALL and HBDG. Through a site survey, SVF and GnPR were calculated. By using multi-criteria linear regression and machine learning, this research investigated five regression models for prediction of outdoor air temperature including linear regression (LR), k-nearest neighbours (KNN), support vector regression (SVR), decision tree (DT) and random forests (RF). The analysis of variables by best subsets regression showed greenery played crucial role in the mitigation of both daytime and night-time UHI. Pedestrian level wind flow was helpful in heat release in the daytime. High-rise buildings provided self-shadowing to reduce ambient air temperature but higher SVF was harmful to heat release in the night-time. For regression models, RF had the best predictive performance. Average RMSE of RF was reduced by 4% to 29% compared to linear regression. The learning curve indicated that the predictive power of LR could not be improved by additional data provision. In contrast, the downward trend in bias and variance suggested that RF can benefit from the training of big data. During the deployment of learning algorithms, RF continued to outperform other learning algorithms.
Mass melting and pair production in strong fields

The electron's electromagnetic and Higgs mass contents respond differently to strong external fields. In the Born-Infeld (BI) model the EM mass melts entirely in the presence of a critical external electric field. For strong quasi-constant electromagnetic fields we apply this effect to compute an enhancement of pair production rates allowing direct measurement of mass melting. Virtual electron mass melting using the perturbative loop QED insert resolves the muon anomalous magnetic moment discrepancy.
New Algorithms for Mixed Dominating Set

A mixed dominating set is a collection of vertices and edges that dominates all vertices and edges of a graph.We study the complexity of exact and parameterized algorithms for MDS, resolving some open questions. In particular, we settle the problem's complexity parameterized by treewidth and pathwidth by giving an algorithm running in time $O^*(5^{tw})$ (improving the current best $O^*(6^{tw})$), as well as a lower bound showing that our algorithm cannot be improved under the SETH, even if parameterized by pathwidth (improving a lower bound of $O^*((2 - \varepsilon)^{pw})$). Furthermore, by using a simple but so far overlooked observation on the structure of minimal solutions, we obtain branching algorithms which improve both the best known FPT algorithm for this problem, from $O^*(4.172^k)$ to $O^*(3.510^k)$, and the best known exponential-time exact algorithm, from $O^*(2^n)$ and exponential space, to $O^*(1.912^n)$ and polynomial space.
Factor Differentials in contraceptive use and demographic profile among females who had their first coital activity at most 16 years versus those at 16+ years old in a developing nation

Background: Previous studies have examined age at first sexual intercourse and factors which determine contraceptive use, but none have explored factors which determined method of contraception use between females whose first coital activity began at 16+ years and those who started < 16 years old. Aims/objectives: This research aims to bridge the gap in the literature by elucidating information on the differentials in factors of contraceptive use between females whose first coital activity was < 16 years and 16+ years old as well as sociodemographic and reproductive health characteristics of these respondents. Methods and materials: This descriptive cross-sectional study used a secondary dataset from a national probability sample survey. The current study extracted only females aged 15-49 years from 2002 Reproductive Health Survey to carry out this research. The study population was 7,168 women of the reproductive ages, with a response rate of 77.6%. Of those who responded, 32.5% had first coitus before 16 years old compared with 67.5% who began at 16+ years. Stepwise logistic regression analyses were used to fit the one outcome measure (contraceptive use) by different socio-demographic as well as reproductive health variables. Odds ratios were determined by logistic regression analyses. A p value < 0.05 (two-tailed) was used to establish statistical significance. Results: More females whose first coitus was < 16 were currently in a sexual union (83%) compared with 79% of those who began at 16+ years old. Factor differentials on contraceptive use emerged between the two cohorts. These were social class (upper class: OR = 0.72, 9%% CI = 0.55 – 0.94) for those who begin < 16 years old but not for those 16+ and area of residence (Rural area: OR = 1.26, 95% CI = 1.07 – 1.47) for the latter but not the former. Conclusion: The current results are far reaching and can be used to guide new public health intervention programmes.
Gradual session types

Session types are a rich type discipline, based on linear types, that lift the sort of safety claims that come with type systems to communications. However, web-based applications and micro services are often written in a mix of languages, with type disciplines in a spectrum between static and dynamic typing. Gradual session types address this mixed setting by providing a framework which grants seamless transition between statically typed handling of sessions and any required degree of dynamic typing. We propose GradualGV as an extension of the functional session type system GV with dynamic types and casts. We demonstrate type and communication safety as well as blame safety, thus extending previous results to functional languages with session-based communication. The interplay of linearity and dynamic types requires a novel approach to specifying the dynamics of the language.
Exploiting Oxide Based Resistive RAM Variability for Probabilistic AI Hardware Design

Uncertainty plays a key role in real-time machine learning. As a significant shift from standard deep networks, which does not consider any uncertainty formulation during its training or inference, Bayesian deep networks are being currently investigated where the network is envisaged as an ensemble of plausible models learnt by the Bayes' formulation in response to uncertainties in sensory data. Bayesian deep networks consider each synaptic weight as a sample drawn from a probability distribution with learnt mean and variance. This paper elaborates on a hardware design that exploits cycle-to-cycle variability of oxide based Resistive Random Access Memories (RRAMs) as a means to realize such a probabilistic sampling function, instead of viewing it as a disadvantage.
The Implications of Tail Dependency for Counterparty Credit Risk Pricing

This paper investigates the counterparty credit risk of interest rate swaps positions using the credit valuation adjustment (CVA) measure, and examines the potential dependency relationships between the probability of default (PD) and the exposure at default (EAD). We empirically tested, using interest rate swaption implied market volatilities, three tail dependency models: a Basel Committee (BCBS, 2011b) independent model, a Gaussian copula dependent model, and a Wrong Way Risk (WWR) with copula dependency approach. The results show that the CVA underestimation when using a Gaussian copula for modelling the dependence of PD and EAD is about 51%–362% compared to using WWR, and the underestimation between using the standardised Basel independent model and using the Gaussian copula is about 527%–1609%, including the period of the 2007/2008 crisis. These results have important implications for regulators, financial institutions, and credit risk managers when calculating counterparty risk.
DMSO_A_198547 637..645

Quan Zhang Yuliang Liu Guohua Liu Geng Zhao Zhigang Qu Weiming Yang 1College of Electronic Information and Automation; 2Binhai International Advanced Structural Integrity Research Centre, Tianjin University of Science and Technology, Tianjin 300222, People’s Republic of China; 3College of Electronic Information and Optical Engineering; 4Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, NanKai University, Tianjin, People’s Republic of China; 5Tianjin Medical University Hospital for Metabolic Disease, Tianjin 300070, People’s Republic of China Background: Using artificial intelligence to assist in diagnosing diseases has become a contemporary research hotspot. Conventional automatic diagnostic method uses a conventional machine learning algorithm to distinguish features from which a professional doctor manually extracts features in diagnostic reports. But it can be difficult to collect large amounts of necessary medical data. Therefore, these methods face challenges with efficiency and accuracy. Method: Here, we proposed an automatic diagnostic system based on a deep learning algorithm to diagnose hyperlipidemia by using human physiological parameters. This model is a neural network which uses technologies of data extension and data correction. Firstly, we corrected and supplemented the original data by the method mentioned previously to solve the problem of lacking data. Secondly, the processed data were used to train a deep learning model. Deep learning model can automatically extract all the available information instead of artificially reducing the raw data. Therefore, it can reduce labor costs. The classifiers classify the data by using features previously mentioned. Finally, the system was evaluated with data from a test dataset. Result: It achieved 91.49% accuracy, 87.50% sensitivity, 93.33% specificity, and 87.50% precision with data from the test dataset. Conclusion: The proposed diagnostic method has a highly robust and accurate performance, and can be used for tentative diagnosis. It can automatically diagnose diseases by using human physiological parameters, thereby reducing labor cost, which results in effective improvement of clinical diagnostic efficiency.
Fusing Shape-from-Silhouette and the Sparsity Driven Detector for Camera-Based 3D Multi-Object Localization with Occlusions

Position estimation of multiple objects in a 3D environment poses a challenging task, even more so in the presence of occlusions due to infrastructure. In this paper we present a method to accurately localize up to 10 moving pedestrians by fusing the output of a Sparsity Driven Detector with volumes generated by a Shape-from-Silhouette approach. We also show how occlusion information from a 3D map of the environment can be integrated into our algorithm to further improve performance. We investigate the influence of different camera heights and image sizes on the optimization problem and demonstrate real-time capability for certain configurations. Additionally, our code is made publicly available under an open source license.1
On the term and concepts of numerical model validation in geoscientific applications

Modeling and numerical simulation of the coupled physical and chemical processes observed in the subsurface are the only options for long-term analyses of complex geological systems. This contribution discusses some more general aspects of the (dynamic) process modeling for geoscientific applications including reflections about the slightly different understanding of the termsmodel and model validation in different scientific communities, and about the term and methods ofmodel calibration in the geoscientifc context. Starting from the analysis of observations of a certain part of the perceived reality, the process of model development comprises the establishment of the physical model characterizing relevant processes in a problem-oriented manner, and subsequently the mathematical and numerical models. Considering the steps of idealization and approximation in the course of model development, Oreskes et al. [1] state that process and numerical models can neither be verified nor validated in general. Rather the adequacy of models with specific assumptions and parameterizations made during model set-up can be confirmed. If the adequacy of process models with observations can be confirmed using lab as well as field tests and process monitoring, the adequacy of numerical models can be confirmed using numerical benchmarking and code comparison. Model parameters are intrinsic elements of process and numerical models, in particular constitutive parameters. As they are often not directly measurable, they have to be established by solving inverse problems based on an optimal numerical adaptation of observation results. In addition, numerical uncertainty analyses should be an obligatory part of numerical studies for critical real world applications.
Country Specific Factors: Public Sector Accounting and Financial Reporting Reforms in Sri Lanka

New Public Management led the world of the public sector including Sri Lanka to switch on to the accrual-based accounting which is referred to as Public Sector Accounting and Financial Reporting reforms. However, it was noted that most of such reform activities have failed. Besides, irrespective of failure recorded Sri Lanka continues with the reform process. This postures the research problem of what factor(s) forces Sri Lanka to endure the reform. As a result, this study in general attempts to hoist the factors that determine the reform and precisely attempts to elevate the country specific factors of the phenomenon in Sri Lankan context based on qualitative methodology employing semi-structured interviews. The data were analyzed using thematic analysis method. The study found that though most of the universal and countryspecific factors are negatively influence the reform in Sri Lanka it is compelled to reform since ‘change of country status’ dictates.
Where Do Software Security Assurance Tools Add Value?

In developing security information technology products, we are presented with a wide choice of development and assurance processes, and of tools and techniques to support those processes. By considering a structured break-down of the goals of a development, and building on the results of a survey of the applicability of tools to certification, this paper proposes a framework for assessing the value of tools – both security specific and more general – to security assurance.