Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoke Gets in Your Eyes: Medical Marijuana Laws and Tobacco Use

The public health costs of tobacco consumption have been documented to be substantially larger than those of marijuana use. This study is the first to investigate the impact of medical marijuana laws (MMLs) on tobacco cigarette consumption. First, using data from the National Survey of Drug Use and Health (NSDUH), we establish that MMLs induce a 2 to 3 percentage-point increase in adult marijuana consumption, likely for both recreational and medicinal purposes. Then, using data from the NSDUH, the Behavioral Risk Factor Surveillance System (BRFSS), and the Current Population Survey Tobacco Use Supplements (CPS-TUS), we find that the enactment of MMLs leads to a 1 to 1.5 percentage-point reduction in adult cigarette smoking. We also find that MMLs reduce the number of cigarettes consumed by smokers, suggesting effects on both the cessation and intensive margins of cigarette use. Our estimated effect sizes imply substantial MML-induced tobacco-related healthcare cost savings, ranging from $4.6 to $6.9 billion per year.
Developing A Complete And Accessible CFD Simulation Platform For Building Applications

Computational Fluid Dynamics (CFD) is widely used as a consequence of increasing computer power, an ever more stringent regulatory framework and the refinement of client’s expectations, both to insure that certain goals are met (e.g., ventilation effectiveness) and to devise means to do so optimally. However, given the areas where CFD was firstly used, like aerospace engineering, most tools available today have not been developed with buildings in mind. Instead, they are largely the result of the big investment made in general purpose CFD codes. This has granted these tools enough modelling capabilities to tackle the most demanding of problems in the buildings arena, but it has also imparted some hindrances like high cost, long training periods, if not altogether cumbersome interfaces, and development priorities aligned with other application areas. In this paper we thus describe some technical details pertaining to a tool which can be taken as a paradigm to overcome the aforementioned difficulties in building applications. The tool is commercially available and is under active development with clear aims: deliver a specialized interface for ease of use and efficiency gains, incorporate several best-practices, provide a complete physical modelling framework to cover present and future needs, enable flexible and scalable computational capacity, allow easy and efficient interoperability with CAD packages and, last but not least, do all of the above at a cost that all parties can accommodate. In this paper we discuss technical details in four areas: overall user-interface technology, done on top of Open-Source packages including automatic aids for results analysis; physical modelling features provided by an OpenFOAM-based CFD engine; considerations on the use of both local and cloud-based computing resources and the implemented CAD interoperability technologies. Simulation results are presented for some validation test-cases. Limitations and difficulties are also presented from the CFD, software development and end-user perspectives. The authors aim to help show one approach to overcome some of the hurdles affecting CFD use and thus contribute to improve building design and operation.
Environmental Chemistry EVALUATING THE EXTENTOF PHARMACEUTICALS IN SURFACEWATERS OF THEUNITED STATES USING A NATIONAL-SCALE RIVERS AND STREAMS ASSESSMENT SURVEY

To assess the potential exposure of aquatic ecosystems to active pharmaceutical ingredients, the authors conducted a national-scale, probability-based statistical survey of the occurrence of these compounds in surface waters of the United States. The survey included 182 sampling sites and targeted riverswith close proximity to urban areas. The 46analytes reported representmany classes of active pharmaceutical ingredients (APIs), including antibiotics, diuretics, antihypertensives, anticonvulsants, and antidepressants.Of the46analytes, 37weredetected in at least 1 sampling location. Sulfamethoxazole (an antibiotic) was themost frequently detected compound, beingmeasured in 141 of the 182 surface waters surveyed at concentrations ranging up to 570 ng/L. Ten of the compounds were detected in 20% or more of the sampling sites. Weighted means of the analytical measurements are used with the statistical survey design and analysis to provide national estimates of the extent of contamination for these APIs in the nation’s urban rivers. Environ Toxicol Chem 2015;9999:1–8. Published 2015Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work and is in the public domain in the United States of America.
Credit Card fraud detection using classification techniques

: Credit card fraud has increased considerably due to the growth of the latest technologies and global communications routes. Credit cards cost billions of dollars in consumer and financial companies per year. Con artists try to find new plans and procedures to continue illegal operations. Therefore, fraud detection systems are necessary for banks and financial institutions to reduce losses. The most common technique used to create fraud detection patterns Furthermore, detecting and preventing credit card fraud is one of the most important problems in the digital world that requires precise transaction analysis. One way to detect fraud is to investigate suspicious changes in user behavior. We have implemented a linear biased classifier using kernel method that allows us to classify data based upon selective attributes by performing vectorization of values and using probability threshold that is set to 90 % based upon which it classifies record as fraud or non fraud. We have compared our results with other techniques such as Bayesian networks, C45, J48 and decision tree along with Bay minimum risks. Our method to improve fraud detection in Credit Cards is the main objective of this work with improvement in current fraud detection process by improving fraudulent account prediction. In addition, there is a collection and discussion of the evaluation criteria used in literature. Therefore, the issue opened for detecting credit card fraud will be described as a guideline for new researchers .
EXTENSION OF THE NON-LOCALLY REACTING MODEL BASED ON THE TRANSFER MATRIX METHOD TO CYLINDRICAL SOUND PACKAGES

This paper presents an extension of the non-locally reacting (NLR) model based on the Transfer Matrix Method (TMM) to cylindrical noise control treatment (NCT). The NLR-TMM model is based on an integral formulation that uses a set of Green functions (fundamental solutions) of the NCT. The latter is assumed to be homogeneous, flat and laterally unbounded. Under these conditions, the TMM is employed in order to estimate the Green functions. Such simplification may be justified by the large dissipation and the softness of the materials usually involved in the NCT. A nodal mesh is used to discretize the trimmed surface of the SP and a geodesic distance between nodes is defined in order to respect the helical patterns of the propagating waves inside the cylindrical NCT. The proposed model of the NCT is combined with a Finite Element model (FEM) of the structure and cavity in a Patch Transfer Functions framework. A trimmed cylindrical shell excited by a point force and coupled to an acoustic cavity is used in order to validate the proposed model. The employed NCT is made of a melamine foam layer and a mass-layer. The results are systematically compared with a full FEM model. It is observed that the inherent unwrapped approximation used to estimate the Green functions has a negligible effect on the accuracy of the proposed model. This indicates that the curvature is mainly governed by the structure. Moreover, the limitations of the locally reacting models are also pointed out.
Mini-Workshop: Innovative Trends in the Numerical Analysis and Simulation of Kinetic Equations

In multiscale modeling hierarchy, kinetic theory plays a vital role in connecting microscopic Newtonian mechanics and macroscopic continuum mechanics. As computing power grows, numerical simulation of kinetic equations has become possible and undergone rapid development over the past decade. Yet the unique challenges arising in these equations, such as high-dimensionality, multiple scales, random inputs, positivity, entropy dissipation, etc., call for new advances of numerical methods. This mini-workshop brought together both senior and junior researchers working on various fast-paced growing numerical aspects of kinetic equations. The topics include, but were not limited to, uncertainty quantiﬁcation, structure-preserving meth-ods, phase transitions, asymptotic-preserving schemes, and fast methods for kinetic equations.
Credit Bubbles and Misallocation

Why are bubbles harmful to the economy? According to a recent literature on rational bubbles, the problem is the burst that causes a misallocation of funds and triggers a recession. This paper proposes a theory of rational bubbles where the boom, not the ensuing bust, reduces the output by promoting a misallocation of factors. As in recent literature, financial markets are imperfect and the rise of a bubble alleviates credit constraints and boosts capital accumulation. However, capital accumulation occurs in unproductive sectors and aggregate output is reduced. The result is driven by the fact that heterogeneous borrowers have an advantage with respect to issuing different types of debt contracts. In normal times, High-productive borrowers have higher collateral and thereby attract most of the funds. In bubbly times, borrowers can also issue “bubbly debt,” a debt that is repaid with future debt. The possibility to keep a pyramid scheme and raise bubbly debt depends on the probability of surviving in the market. Therefore, a bubble misallocates resources towards borrowers with low fundamental risk, even if they invest in projects with lower productivity. An augmented version of the model with nominal rigidities is proposed to explain the timing of expansions and recessions during “bubbly episodes”: the initial boom in output is caused by a positive demand effect; the long run reduction in TFP is driven by a misallocation process. The theory is supported by evidence on between-industry misallocation in the years preceding the 2008 financial crisis. JEL classification: E32, E44, O16
Measuring and Modeling the Label Dynamics of Online Anti-Malware Engines

VirusTotal provides malware labels from a large set of anti-malware engines, and is heavily used by researchers for malware annotation and system evaluation. Since different engines often disagree with each other, researchers have used various methods to aggregate their labels. In this paper, we take a data-driven approach to categorize, reason, and validate common labeling methods used by researchers. We ﬁrst survey 115 academic papers that use VirusTotal, and identify common methodologies. Then we collect the daily snapshots of VirusTotal labels for more than 14,000 ﬁles (including a subset of manually veriﬁed ground-truth) from 65 VirusTotal engines over a year. Our analysis validates the beneﬁts of threshold-based label aggregation in stabilizing ﬁles’ labels, and also points out the impact of poorly-chosen thresholds. We show that hand-picked “trusted” engines do not always perform well, and certain groups of engines are strongly correlated and should not be treated independently. Finally, we empirically show certain engines fail to perform in-depth analysis on submitted ﬁles and can easily produce false positives. Based on our ﬁndings, we offer suggestions for future usage of VirusTotal for data annotation.
The unusual Seyfert Markarian 926 – a link to the LINERs?

Markarian 926 (= MCG-2-58-22) is one of the earliest Seyfert galaxies identified. At discovery it was one of the most luminous nearby active galactic nuclei (AGN), with strong, wide broad lines. In the late 1980's it started fading, eventually settling at barely ~10% of its recorded peak luminosity. The luminosity decrease was accompanied by significant spectral changes, with the broad-line component now much weaker and highly asymmetrical. Low ionisation narrow lines, however, are now unusually strong, more typical of the AGN class referred to as LINERs rather than a Seyfert. This peculiar low-luminosity phase spectrum has remained relatively constant over the last decade. The paper discusses the spectral and other characteristics of Markarian 926 and compares these to standard Seyfert models. It furthermore investigates Markarian 926's relationship to the LINERs, and whether SALT observations of this object could shed light on the interrelationship between Seyferts and LINERs.
Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation

Machine learning has been widely applied to building security applications. However, many machine learning models require the continuous supply of representative labeled data for training, which limits the models’ usefulness in practice. In this paper, we use bot detection as an example to explore the use of data synthesis to address this problem. We collected the network traffic from 3 online services in three different months within a year (23 million network requests). We develop a stream-based feature encoding scheme to support machine learning models for detecting advanced bots. The key novelty is that our model detects bots with extremely limited labeled data. We propose a data synthesis method to synthesize unseen (or future) bot behavior distributions. The synthesis method is distribution-aware, using two different generators in a Generative Adversarial Network to synthesize data for the clustered regions and the outlier regions in the feature space. We evaluate this idea and show our method can train a model that outperforms existing methods with only 1% of the labeled data. We show that data synthesis also improves the model’s sustainability over time and speeds up the retraining. Finally, we compare data synthesis and adversarial retraining and show they can work complementary with each other to improve the model generalizability.
Trend inflation meets macro-finance: the puzzling behavior of price dispersion

Motivated by recent empirical findings that emphasize low-frequency movements in inflation as a key determinant of term structure, we introduce trend inflation into the workhorse macro-finance model. We show that this compromises the earlier model success and delivers implausible business cycle and bond price dynamics. We document that this result applies more generally to non-linearly solved models with Calvo pricing and trend inflation and is driven by the behavior of price dispersion, which is i) counterfactually high and ii) highly inaccurately approximated. We highlight the channels behind the undesired performance under trend inflation and propose several remedies.
Deep Reinforcement Learning and Forward Modeling for StarCraft AI

net to exploit that structure optimally. In particular we do not need to explicitly encode the relative positions of units: their positions are directly visible on the 2D map. Having all the units appear on a 2D image also enables eﬃcient handling of collisions between nearby units, as well as complex actions which have areas of eﬀect (for instance a Protoss High Templar’s Psi Storm)
International Journal of Social Science and Economic Research

The traditional goldsmith community in Kerala has come to a turnaround in their socioeconomics from good to bad levels. This is primarily due to twin effects, the first of it is globalisation of the gems and jewellery sector in the beginning of the 1990s and the second and the most important is the technological innovation and mechanisation with heavy doses of capital investment. In this, the poor traditional goldsmith community faces the major brunt in their socio economics as they are unaware of the global cues in the sector inter alia their poor financial base to embrace the new changes in technology. All these make the situation vulnerable and therefore the community perceives to have an unfavourable future. The study is based on primary data amassed from 300 goldsmiths from the districts of Kasargod and Thrissur based on random sampling methods. Though, the study is more connected to the socio-economics of the community, it uses the conventional theoretical aspects of innovation as in the works of Lundvall (1992), Freeman (1995) and the product adaptation explanations of Rogers (2003). The result of the study shows that there are rampant downward shifts in their socio economics as the income earning from the avocation of the community is below the state average; hence the youngsters are unwilling to continue this traditional work. This is the reason for their weak perception for a sustainable livelihood from the sector.
The Impact of Reward and Knowledge Sharing Practices on Employee Performance: A Comparative Analysis between Awash and Dashen Bank S.C.

Employee performance is measured by the quality of work, productivity, ability to work independently with little or no direction or follow-up to complete tasks, job knowledge, employees’ interpersonal relationships and so on. Reward systems have long been used as a strategy to motivate employees and increase their performance. Several studies have also suggested that knowledge sharing increases learning which improves employees’ job performance. Yet, in Ethiopia, several organizations haven’t done a comprehensive study on which type of reward system or knowledge sharing practice best fit them. The purpose of this study is to examine the reward system and knowledge sharing practices of two banks, Awash and Dashen, in Adama Ethiopia, and analyze the differences in their employee performance. To achieve this objective this study uses instruments based on intrinsic and extrinsic reward system and tacit and explicit knowledge sharing and their impact on employee performance. The target population are employees of Awash and Dashen bank in Adama, Ethiopia. An empirical study using qualitative data and survey method is employed to conduct this study. A convenient sample is collected from 120 employees in each bank. The theoretical and practical implications of the study are also discussed.
Immigrant children’s construction of their identity: The case of African children

This paper examines how the children of African immigrants construct their identity in the United States. The U.S. immigration policies allowing citizens of foreign countries to migrate into the country, have favored the immigration of many Africans into the country. Many of those immigrants experience challenges ranging from language and economic issues to social and cultural integration. The challenges are more accrued for the children who have to juggle between their cultural backgrounds reinforced at home and the values available in their new cultural and social environment. While there is a large body of literature dealing with issues of identity faced by immigrant children in general and English language learners in particular, the case of African children has remained unexplored. Because of the linguistic diversity in African countries, most of the children spoke at least one language other than English before immigration, they do not always have the space to practice it as their languages and cultures are marginalized both at school and in the society. In this paper, I investigate the following questions: To what extent are African children familiar with their social and cultural backgrounds? How do children identify themselves? What are the factors that impact on the children’s identity? To which extent do parents influence their children’s identification of the self?
Receptivity: Team Description Paper 2018 Fine Tuning of Agent Decision Evaluation

Receptivity is a new team for Robocup 2019 in the 2D simulation league. It is based on agent2d-3.1. 1 and the recently released Gliders2d. The strategic focus of the team is on improving the evaluation function with machine learning and reinforcement learning techniques. In particular, ﬁne tuning of the agent decision can be made by identifying situations where the intended action was not successfully completed and instead led to negative outcomes such as loss of ball possession. This paper outlines the process of learning and evaluation for decision ﬁne tuning.
Impact of Education and Other Potential Covariates on Nutritional Status in Lebanese Rural Elderly Women

Background: In Lebanon, an Arab Mediterranean country, nutritional status of elderly population was described as pooled results from both genders, underestimating the influence of education on nutritional status in Lebanese rural elderly women. Objective: We aimed to study the impact of education and other covariates on nutritional status in Lebanese elderly women. Materials and Methods: This cross-sectional study included a representative sample of 607 community dwelling elderly women from all rural districts in Lebanon. Data included socio demographic factors, health related characteristics [comorbidities, chronic pain, ADL disabilities etc.], mental health status [5 item GDS, MMS], social isolation [LSNS 6 scale] and variables related to food intake. Nutritional status was assessed through the Mini Nutritional Assessment. Results: After identifying significant associated factors in the bivariate analysis, a stepwise multiple logistic regression found a strong and independent relationship between the different levels of education versus illiteracy, and nutritional status [OR: 0.58; 95% CI: 0.35-0.96 and OR: 0.42; 95% CI: 0.21-0.83]. Social isolation, low physical health status, depression, cognitive dysfunction were other significantly associated covariates. Furthermore, women participating in meal preparation were at lower risk of poor nutritional status [OR: 0.30; 95% CI: 0.18-0.50]. Conclusion: We demonstrated that educational status in women was a strong predictor of poor nutritional status in this representative sample of rural elderly women. We should therefore emphasize on the importance of access to education and information in particular in female population living in rural areas.
Assessing the environmental advantages of high strength steel

High strength steel grades often have a larger environmental impact compared to ordinary steels when just considering the "cradle-to-gate" results per tonne steel. This is generally due to larger alloy content and/or more complex process routes. On the other hand, less material might be required to fulfil a specific function and the life-span of the steel product might increase. This often means that the environmental impacts of high strength steel used in an application, is lower than if ordinary steels were used. It is therefore important not to compare different steel grades based on the cradle-to-gate results per tonne steel. Using less material in a product reduces the quantity of materials produced, and also affects the use phase of the product life cycle for active applications such as vehicles. There is a huge potential for savings in fuel consumption due to weight reduction in vehicles upgraded to high strength steel.
PhaseMax: Convex Phase Retrieval Without Lifting

where ai ∈ H, and i = 1, 2, . ,m are measurement vectors. While the recovery of x from (1) is non-convex, it can be convexified via “lifting” methods that convert the phase retrieval problem to a semidefinite program (SDP). The resulting formulation is convex, but this convexity comes at a high cost: lifting methods square the dimensionality of the problem. As a result, many practical phase retrieval algorithms sacrifice convexity and operate in the original feature space using non-convex methods [2]–[6]. We propose PhaseMax, a formulation of the phase retrieval problem that avoids lifting [7], [8]. Suppose we have some intelligent “guess” x̂ ∈ H of the solution to (1). We recover the signal x by solving the following convex optimization problem: