nils-herrmann/clean_scirepeval huggingface dataset:
Precise and scalable static analysis of jQuery using a regular expression domain

jQuery is the most popular JavaScript library but the state of the art static analyzers for JavaScript applications fail to analyze simple programs that use jQuery. In this paper, we present a novel abstract string domain whose elements are simple regular expressions that can represent prefix, infix, and postfix substrings of a string and even their sets. We formalize the new domain in the abstract interpretation framework with abstract models of strings and objects commonly used in the existing JavaScript analyzers. For practical use of the domain, we present polynomial time inclusion decision rules between the regular expressions and prove that the rules exactly capture the actual inclusion relation. We have implemented the domain as an extension of the open source JavaScript analyzer, SAFE, and we show that the extension significantly improves the scalability and precision of the baseline analyzer in analyzing programs that use jQuery.
An experimental study on rheological behavior of non Newtonian hybrid nano coolant for application in cooling and heating systems

In this paper, the rheological behavior of COOH functionalized MWCNTs SiO2/EG water hybrid nano coolant for application in cooling systems at temperatures ranging from 27.5 Ct o 50C has been examined. Stable and homogeneous suspensions, with solid volume fractions ranging from 0.0625% to 2% were prepared by dispersing dry MWCNTs and SiO2 nanoparticles (50:50 vol. in a specified amount of a binary mixture of EG water (50:50 vol. Viscosity measurements were performed at the shear rate range of 0.612 s 1 to 122.3 s 1 for each nano coolant sample. Results showed that the base fluid exhibits Newtonian behavior and the nano coolant samples exhibit a pseudoplastic rheological behavior with a power law index of less than unity (n 1) The results also revealed that the apparent viscosity generally increases with an increase in the solid volume fraction and decreases with increasing temperature.
Learning Approach to Cycle Time Minimization of Wood Milling Using Adaptive Force Control

A majority of the machining processes in the industry of today is performed using position controlled machine tools, where conservative feed rates have to be used in order to avoid excessive process forces. By instead controlling the process forces, the feed rate, and consequently the material removal rate, can be maximized. In turn, this leads to decreased cycle times and cost savings. Furthermore, path planning with respect to time minimization for milling processes, especially in non isotropic materials, is not straightforward. This paper presents a model based adaptive force controller that achieves optimal feed rates, in combination with a learning algorithm to obtain the optimal machining path, in terms of minimizing the milling duration. The proposed solution is evaluated in both simulation and experiments, where an industrial robot is used to perform rough cut wood milling. Cycle time reductions of 14% using force control compared to position control were achieved, and on average an additional 28% cycle time reduction with the proposed learning algorithm.
Women in higher education and academia in Iran

This article addresses women's contemporary position in academia in Iran. By systematically reviewing available academic and official databases on women's positions in academia published online or in print, the question is raised if women's current position and role in academia is the result of personal choice making or an existent systematic discriminatory social structure. To address this issue, available Iranian research and data on female recruitment in universities are analyzed. The results show that there is a general accordance on two findings in the research addressing women's position in academia. First, gender discrimination is restricting women's choices by systematically excluding them from educational, managerial and administrative positions. Second, in spite of the substantial increase in women's enrolment in tertiary education, significant development in women's position and role in scientific and educational institutions has not taken place. After discussing the data, I will try to compare two sets of sociological theoretical approaches that offer explanations for women's lower levels of participation in the academia; namely, theories of exclusion and theories of participation. I suggest that the former is more effective in analyzing women's position in Iranian academia, because of being based on recognition of the existing structural discriminations. Although women's participation in higher education is gradually growing and gender boundaries are being stretched on a daily basis, there is need for fundamental structural changes in social and educational spheres, and widespread implementation of positive discrimination.
Beyond Journal Impact and Usage Statistics: Using Citation Analysis for Collection Development

This article outlines a methodology to generate a list of local core journal titles by doing a citation analysis and details the process for retrieving and downloading data from Scopus. It analyzes correlations among citation count, journal rankings, and journal usage. The results of this study reveal significant correlations between journal rankings and journal usage. No correlation with citation count has been found. Limitations and implications for collection development and outreach are also discussed.
User Centered Collection Development: A Citation Analysis of Graduate Biology Theses

The authors sought to identify biology collection usage among graduate students at Jacksonville State University. Forty master's degree student theses from 2008 to 2014 were examined. A total of 2,351 citations were analyzed, of which journals accounted for 75% books for 10% and the remaining 15% included government documents, websites, dissertations, and theses. Findings are consistent with previous similar studies: students referenced journals more often than books and other sources. Journal and book citations present discrepancies between number of citations and number of authors citing. Wide ranges of journal subjects were cited due to the interdisciplinary nature of graduate programs. Recommendations are provided for future collection development and library instruction practice.
DNA Methylation Signatures of Early Childhood Malnutrition Associated With Impairments in Attention and Cognition
Venue: Biological Psychiatry

Early childhood malnutrition affects 113 million children worldwide, impacting health and increasing vulnerability for cognitive and behavioral disorders later in life. Molecular signatures after childhood malnutrition, including the potential for intergenerational transmission, remain unexplored.  We surveyed blood DNA methylomes ~483,000 individual CpG sites) in 168 subjects across two generations, including 50 generation 1 individuals hospitalized during the first year of life for moderate to severe protein energy malnutrition, then followed up to 48 years in the Barbados Nutrition Study. Attention deficits and cognitive performance were evaluated with the Connors Adult Attention Rating Scale and Wechsler Abbreviated Scale of Intelligence. Expression of nutrition sensitive genes was explored by quantitative reverse transcriptase polymerase chain reaction in rat prefrontal cortex.  We identified 134 nutrition sensitive, differentially methylated genomic regions, with most (87% specific for generation 1. Multiple neuropsychiatric risk genes, including COMT, IFNG, MIR200B, SYNGAP1, and VIPR2 showed associations of specific methyl CpGs with attention and IQ. IFNG expression was decreased in prefrontal cortex of rats showing attention deficits after developmental malnutrition.  Early childhood malnutrition entails long lasting epigenetic signatures associated with liability for attention and cognition, and limited potential for intergenerational transmission.
Naevus spilus type congenital melanocytic naevus associated with a novel NRAS codon 61 mutation
Venue: The British journal of dermatology

We recently described 17 patients with a new subtype of congenital melanocytic nevi (CMN) designated as 'naevus spilus type CMN' The affected patients present with a large caf e au lait background macule and dozens of superimposed small and medium sized CMN. Here we report an additional case with a novel NRAS mutation (Q61L) Congenital melanocytic naevi are categorized by their largest diameter (projected adult size, PAS) Small CMN 1 5 cm PAS) and medium sized CMN (1 5 20 cm PAS) represent rather common, solitary, slightly thickened plaques with prominent terminal hairs. In contrast, large CMN (20 40 cm PAS) and giant CMN 40 cm PAS) are often accompanied by disseminated
Occupancy behavior based model predictive control for building indoor climate A critical review

This paper reviews occupancy based model predictive control (MPC) for building indoor climate control. Occupancy behavior in buildings is stochastic and complex in nature. With better understanding of occupancy presence in rooms and spaces, advanced controls, such as MPC, can be designed to achieve a more energy efficient operation, compared to more traditional control methods, while comfort is maintained. This paper starts with an overview of traditional controls implemented in buildings, and importance of occupancy based controls. Various control oriented building modeling methods including physics based and data driven models are reviewed. Later on, a comprehensive review of MPC in terms of control theory, objective functions, constrains, optimization methods, system characteristics and various types of MPC is presented conducted. In principle, MPC finds an optimal sequence of control commands to optimize an objective function, considering system model, disturbances, predictions and actuation constraints. Lastly, occupancy based controls including commonly used rule based and latest model based controls are reviewed. In addition, a few experimental studies are presented and discussed. The paper presents a holistic overview of occupancy based MPC for building heating, ventilation, and air conditioning (HVAC) systems, and discusses current status and future challenges. The purpose of this paper is to provide a guideline forresearchers who would like to conduct similar studies to have a better understanding of established research methods.
Ecological Adaptive Cruise Controller for Plug In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control
Venue: IEEE Transactions on Intelligent Transportation Systems

Plug in hybrid electric vehicles (PHEVs) are promising options for future transportation. Having two sources of energy enables them to offer better fuel economy and fewer emissions. Significant research has been done to take advantage of future route information to enhance vehicle performance. In this paper, an ecological adaptive cruise controller (Eco ACC) is used to improve both fuel economy and safety of the Toyota Prius Plug in Hybrid. Recently, an emerging trend in the research has been to improve the adaptive cruise controller. However, the majority of research to date has focused on driving safety, and only rare reports in the literature substantiate the applicability of such systems for PHEVs. Here, we demonstrate that using an Eco ACC system can simultaneously improve total energy costs and vehicle safety. The developed controller is equipped with an onboard sensor that captures upcoming trip data to optimally adjust the speed of PHEVs. The nonlinear model predictive control technique (NMPC) is used to optimally control vehicle speed. To prepare the NMPC controller for real time applications, a fast and efficient control oriented model is developed. The authenticity of the model is validated using a high fidelity Autonomie based model. To evaluate the designed controller, the global optimum solution for cruise control problem is found using Pontryagin's minimum principle (PMP) To explore the efficacy of the controller, PID and linear MPC controllers are also applied to the same problem. Simulations are conducted for different driving scenarios such as driving over a hill and car following. These simulations demonstrate that NMPC improves the total energy cost up to 19%
FPGA Based Model Predictive Controller for Direct Matrix Converter
Venue: IEEE Transactions on Industrial Electronics

Recently, there has been an increase in the use of finite control set model predictive control (FCS MPC) for power converters. Model predictive control (MPC) uses the discrete time model of the system to predict future values of control variables for all possible control actions and computes a cost function related to control objectives. This control technique can provide fast dynamic response. However, MPC method implementation imposes a very high computational burden and causes significant hardware requirements for real time implementation. In this paper, a fully field programmable gate array (FPGA) based real time implementation of MPC is proposed for direct matrix converter (DMC) In the proposed method, all control calculations and the safe commutation scheme for DMC are fully implemented in the FPGA and the need for another digital control platform, such as digital signal processors (DSP) or dSPACE, is eliminated. The proposed scheme takes full advantages of the parallel computation capability of FPGAs.
An Inversion Free Predictive Controller for Piezoelectric Actuators Based on a Dynamic Linearized Neural Network Model
Venue: IEEE/ASME Transactions on Mechatronics

Piezoelectric actuators (PEAs) are widely used in high precision positioning applications. However, the inherent hysteresis nonlinearity seriously deteriorates the tracking performance of PEAs. To deal with it, the compensation of the hysteresis by using its inverse model (called inversion based) is the popular method in the literature. One major disadvantage of this method is that the tracking performance of PEAs highly relies on its inverse model. Meanwhile, the computational burden of obtaining the inverse model is overwhelming. In addition, the physical constraints of the input voltage of PEAs is hardly handled by the inversion based method. This paper proposes an inversion free predictive controller, which is based on a dynamic linearized multilayer feedforward neural network (MFNN) model. By the proposed method, the inverse model of the inherent hysteresis is not required, and the control law can be obtained in an explicit form. By using the technique of constrained quadratic programming, the proposed method still works well when dealing with the physical constraints of PEAs. Moreover, an error compensation term is introduced to reduce the steady state error if the dynamic linearized MFNN cannot approximate the PEA's dynamical model satisfactorily. To verify the effectiveness of the proposed method, experiments are conducted on a commercial PEA. The experiment results show that the proposed method has a satisfactory tracking performance even with high frequency references. Comparisons demonstrate that the proposed method outperforms some existing results.
On Using Pareto Optimality to Tune a Linear Model Predictive Controller for Wind Turbines

Optimal operation of wind turbines is important in order to minimize cost of energy, which is one of the major focus areas of the wind industry. Model predictive control (MPC) is a candidate for a control solution which effectively balances the different and potentially conflicting objectives, e.g. generated power and structural loads. This article presents a method on how to tune multi objective MPC problems using Pareto curves. The approach is applied to a realistic wind turbine MPC problem, in which a joint power and tower fore aft fatigue load optimization is performed. The controller is evaluated on a high fidelity model using a Vestas wind turbine simulator. In addition to the multiple control objectives, a number of constraints are considered as well. The evaluation shows a good potential of using model predictive control for this problem compared with an industrial baseline controller as, it approximately obtains the same mean generated power, while lowering the tower fore aft fatigue loads. The computed Pareto curves of the trade off between tower fore aft fatigue load and mean generated power for a number of different weight matrices, demonstrate a potential tool for tuning MPC solutions for a wind turbine.
Predicting the Viscosity of Hydrocarbon Mixtures and Diluted Heavy Oils Using the Expanded Fluid Model

The expanded fluid (EF) viscosity model was recently developed for petroleum applications. While petroleum fluids can be modeled as a single component fluid, process calculations often require that the fluid be treated as a mixture of pseudo components. For mixtures, the EF model mixing rules require binary interaction parameters, aij, between each component pair in the mixture, which are fitted to experimental data. In this study, a generalized correlation is developed for these interaction parameters as a function of the specific gravity and hydrogen/carbon ratio of the mixture components. The proposed correlation was developed on the basis of a data set that included viscosity and density literature data for pure hydrocarbon pairs at room temperature and atmospheric pressure and new data for pseudo pairs of heavy oil/solvent at temperatures from 21 to 175 degC, pressures up to 10 MPa, and solvent contents up to 40 wt The correlation was assessed on a distinct test data set that included viscosity data.
Social Media and Social Movements

What role does social media play in social movements and political unrest? Twitter, Facebook, Instagram, and Google have all been cited as important components in social revolutions, including those in Tunisia, Egypt, Iceland, Spain, and the global Occupy movement. This essay explores social science claims about the relationship between social networking and social movements. It examines research done on the relationship between social networking, the promotion of activism, and the offline participation in the streets. Can the technology of social networking help activists to achieve their goals? If so, is it just one of many tools they may use, or is the technology so powerful that the right use will actually tip the scales in favor of the social movement? This scholarship divides into optimistic, pessimistic, and ambivalent approaches, turning on an oft repeated question: will the revolution be tweeted?
Digital Media Use and Social Engagement: How Social Media and Smartphone Use Influence Social Activities of College Students
Venue: Cyberpsychology Behav. Soc. Netw.

Social media and mobile phones have emerged as important platforms for college students' communication activities. This study examined how college students' psychological need to belong is associated with their use of social media and smartphones. In addition, it further investigated the effects of college students' digital media use on their social engagement. Findings revealed that students' need to belong was positively related with their use of social media and smartphones, which could further facilitate their social engagement. Moreover, the relationship between the need to belong and social engagement was mediated by college students' digital media use. This study offers empirical evidence of the positive effects of digital media on social behaviors and contributed to further understanding about the mechanisms by which need to belong leads to social engagement through digital media use.
C C A Framework for Building Composable Zero Knowledge Proofs

Non interactive zero knowledge proofs are a powerful cryptographic primitive used in privacypreserving protocols. We design and build CC, the first system enabling developers to build efficient, composable, non interactive zero knowledge proofs for generic, user defined statements. CC extends state of the art SNARK constructions by applying known strengthening transformations to yield UC composable zero knowledge proofs suitable for modular use in larger cryptographic protocols. To attain fast practical performance, CC includes a library of several "SNARK friendly" cryptographic primitives. These primitives are used in the strengthening transformations in order to reduce the overhead of achieving composable security. Our open source library of optimized arithmetic circuits for these functions are up to 40x more efficient than standard implementations and are thus of independent interest for use in other NIZK projects. Finally, we evaluate CC on applications such as anonymous credentials, private smart contracts, and nonoutsourceable proof of work puzzles and demonstrate 5x to 8x speedup in these application settings compared to naive implementations.
Aero thermo dynamic analysis of a low ballistic coefficient deployable capsule in Earth re entry

The paper deals with a microsatellite and the related deployable recovery capsule. The aero brake is folded at launch and deployed in space and is able to perform a de orbiting controlled re entry. This kind of capsule, with a flexible, high temperature resistant fabric, thanks to its lightness and modulating capability, can be an alternative to the current "conventional" recovery capsules. The present authors already analyzed the trajectory and the aerodynamic behavior of low ballistic coefficient capsules during Earth re entry and Mars entry. In previous studies, aerodynamic longitudinal stability analysis and evaluation of thermal and aerodynamic loads for a possible suborbital re entry demonstrator were carried out in both continuum and rarefied regimes. The present study is aimed at providing preliminary information about thermal and aerodynamic loads and longitudinal stability for a similar deployable capsule, as well as information about the electronic composition of the plasma sheet and its possible influence on radio communications at the altitudes where GPS black out could occur. Since the computer tests were carried out at high altitudes, therefore in rarefied flow fields, use of Direct Simulation Monte Carlo codes was mandatory. The computations involved both global aerodynamic quantities (drag and longitudinal moment coefficients) and local aerodynamic quantities (heat flux and pressure distributions along the capsule surface) The results verified that the capsule at high altitude (150 km) is self stabilizing; it is stable around the nominal attitude or at zero angle of attack and unstable around the reverse attitude or at 180deg angle of attack. The analysis also pointed out the presence of extra statically stable equilibrium trim points.
On projected stochastic gradient descent algorithm with weighted averaging for least squares regression
Venue: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The problem of least squares regression of a d dimensional unknown parameter is considered. A stochastic gradient descent based algorithm with weighted iterate averaging that uses a single pass over the data is studied and its convergence rate is analyzed. We first consider a bounded constraint set of the unknown parameter. Under some standard regularity assumptions, we provide an explicit O(1/k) upper bound on the convergence rate, depending on the variance (due to the additive noise in the measurements) and the size of the constraint set. We show that the variance term dominates the error and decreases with rate 1 /k, while the constraint set term decreases with rate log k/k2. We then compare the asymptotic ratio r between the convergence rate of the proposed scheme and the empirical risk minimizer (ERM) as the number of iterations approaches infinity. We show that r 4 under some mild conditions for all d 1. We further improve the upper bound by showing that r 4/3 for the case of d =1 and unbounded parameter set. Simulation results demonstrate strong performance of the algorithm as compared to existing methods, and coincide with r 4/3 even for large d in practice.