nils-herrmann/clean_scirepeval huggingface dataset:
Sleep and risk-taking behavior in adolescents.

The primary purpose of this study was to examine the relationship between adolescents' sleep-wake patterns and risk-taking behavior. A second goal was to replicate the results obtained by Wolfson and Carskadon (1998) regarding adolescents' sleep habits. Three hundred eighty-eight adolescents (217 males, 171 females) completed the Sleep Habits Survey and the Youth Risk Behavior Survey. The results indicated that adolescents who reported longer weekend delay and higher levels of sleep problems also reported significantly higher levels of risk-taking behaviors, and students' weekend delay was also related to their academic performance in this sample. As in the sample studied by Wolfson and Carskadon (1998), the adolescents in this study exhibited changes in both weekday and weekend sleep habits across grade/age. However in the present study, only school-night total sleep time and weekend delay were related to adolescents' daytime functioning, with no significant relationships being found between weekend oversleep and daytime functioning. This provides partial support for the findings of Wolfson and Carskadon (1998). Overall, sleep-wake patterns were found to relate to risk-taking behavior during adolescence in this study.
Neuronal plasticity and survival in mood disorders

Studies at the basic and clinical levels demonstrate that neuronal atrophy and cell death occur in response to stress and in the brains of depressed patients. Although the mechanisms have yet to be fully elucidated, progress has been made in characterizing the signal transduction cascades that control neuronal atrophy and programmed cell death and that may be involved in the action of antidepressant treatment. These pathways include the cyclic adenosine monophosphate and neurotrophic factor signal transduction cascades. It is notable that these same pathways have been demonstrated to play a pivotal role in cellular models of neural plasticity. This overlap of plasticity and cell survival pathways, together with studies demonstrating that neuronal activity enhances cell survival, suggests that neuronal atrophy and death could result from a disruption of the mechanisms underlying neural plasticity. The role of these pathways and failure of neuronal plasticity in stress-related mood disorders are discussed.
Software testing: a research travelogue (2000-2014)

Despite decades of work by researchers and practitioners on numerous software quality assurance techniques, testing remains one of the most widely practiced and studied approaches for assessing and improving software quality. Our goal, in this paper, is to provide an accounting of some of the most successful research performed in software testing since the year 2000, and to present what appear to be some of the most significant challenges and opportunities in this area. To be more inclusive in this effort, and to go beyond our own personal opinions and biases, we began by contacting over 50 of our colleagues who are active in the testing research area, and asked them what they believed were (1) the most significant contributions to software testing since 2000 and (2) the greatest open challenges and opportunities for future research in this area. While our colleagues’ input (consisting of about 30 responses) helped guide our choice of topics to cover and ultimately the writing of this paper, we by no means claim that our paper represents all the relevant and noteworthy research performed in the area of software testing in the time period considered—a task that would require far more space and time than we have available. Nevertheless, we hope that the approach we followed helps this paper better reflect not only our views, but also those of the software testing community in general.
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma

Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell RNA sequencing (RNA-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.
LTE Capacity Compared to the Shannon Bound

In this paper we propose a modification to Shannon capacity bound in order to facilitate accurate benchmarking of UTRAN long term evolution (LTE). The method is generally applicable to wireless communication systems, while we have used LTE air-interface technology as a case study. We introduce an adjusted Shannon capacity formula, where we take into account the system bandwidth efficiency and the SNR efficiency of LTE. Separating these issues, allows for simplified parameter extraction. We show that the bandwidth efficiency can be calculated based on system parameters, while the SNR efficiency is extracted from detailed link level studies including advanced features of MIMO and frequency domain packet scheduling (FDPS). We then use the adjusted Shannon capacity formula combined with G-factor distributions for macro and micro cell scenarios to predict LTE cell spectral efficiency (SE). Such LTE SE predictions are compared to LTE cell SE results generated by system level simulations. The results show an excellent match of less that 5-10% deviation.
Aspect-Based Opinion Extraction from Customer reviews

Text is the main method of communicating information in the digital age. Messages, blogs, news articles, reviews, and opinionated information abounds on the Internet. People commonly purchase products online and post their opinions about purchased items. This feedback is displayed publicly to assist others with their purchasing decisions, creating the need for a mechanism with which to extract and summarize useful information for enhancing the decision-making process. Our contribution is to improve the accuracy of extraction by combining different techniques from three major areas, named Data Mining, Natural Language Processing techniques and Ontologies. The proposed framework sequentially mines product’s aspects and users’ opinions, groups representative aspects by similarity, and generates an output summary. This paper focuses on the task of extracting product aspects and users’ opinions by extracting all possible aspects and opinions from reviews using natural language, ontology, and frequent “tag” sets. The proposed framework, when compared with an existing baseline model, yielded promising results.
Message authentication in driverless cars

Driverless cars and driver-assisted vehicles are becoming prevalent in today's transportation systems. Companies like TESLA, Audi, and Google are among the leaders in this new era of the automotive industry. Modern vehicles, equipped with sophisticated navigation devices, complex driver-assisted systems, and a whole lot of safety features, bring broader impacts to our quality of life, economic development, and environmental sustainability. Instant safety messages such as pre-collision warnings, blind-spot detection, pedestrian and object awareness significantly improve the safety for drivers, passengers, and pedestrians. As a result, vehicles would be able to travel closely yet safely together, forming a platoon, thus resulting in a reduction of traffic congestion and fuel consumption. Driverless cars also have non-safety-related applications: which are used to facilitate traffic management and infotainment dissemination for drivers and passengers. Vehicular Ad hoc Network (VANET), the wireless communication technology enabling driverless cars, features not only dynamic topology but also high mobility. The vehicle nodes move rapidly on streets and highways. Their movements also depend on but not limited to road traffic, speed limits, and behavior of nearby vehicles. With massive amount of messages exchanged among driverless cars that command the cars' movements at high speeds and in close distances, any malicious alternation could lead to disastrous accidents. Message authentication to ensure data integrity is paramount to attack preparation. This paper presents a novel message authentication scheme that protects cars from bogus messages and makes VANET resilient to Denial-of-Service (DoS) attacks. The work includes a simulation framework that integrates vehicle and data traffic models to validate the effectiveness of the proposed message authentication scheme.
Neural network credit scoring models

This paper investigates the credit scoring accuracy of "ve neural network models: multilayer perceptron, mixture-of-experts, radial basis function, learning vector quantization, and fuzzy adaptive resonance. The neural network credit scoring models are tested using 10-fold crossvalidation with two real world data sets. Results are benchmarked against more traditional methods under consideration for commercial applications including linear discriminant analysis, logistic regression, k nearest neighbor, kernel density estimation, and decision trees. Results demonstrate that the multilayer perceptron may not be the most accurate neural network model, and that both the mixture-of-experts and radial basis function neural network models should be considered for credit scoring applications. Logistic regression is found to be the most accurate of the traditional methods.
Knowledge Acquisition Via Incremental Conceptual Clustering

Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.
Improving the effectiveness of peer feedback for learning

The present study examined the effectiveness of (a) peer feedback for learning, more specifically of certain characteristics of the content and style of the provided feedback, and (b) a particular instructional intervention to support the use of the feedback. A quasi-experimental repeated measures design was adopted. Writing assignments of 43 students of Grade 7 in secondary education showed that receiving ‘justified’ comments in feedback improves performance, but this effect diminishes for students with better pretest performance. Justification was superior to the accuracy of comments. The instructional intervention of asking assessees to reflect upon feedback after peer assessment did not increase learning gains significantly.
Corporate Sustainability : First Evidence on Materiality

An increasing number of companies make sustainability investments, and an increasing number of investors integrate sustainability performance data in their capital allocation decisions. To date however, the prior academic literature has not distinguished between investments in material versus immaterial sustainability issues. We develop a novel dataset by hand-mapping data on sustainability investments classified as material for each industry into firm-specific performance data on a variety of sustainability investments. This allows us to present new evidence on the value implications of sustainability investments. Using calendar-time portfolio stock return regressions we find that firms with good performance on material sustainability issues significantly outperform firms with poor performance on these issues, suggesting that investments in sustainability issues are shareholder-value enhancing. Further, firms with good performance on sustainability issues not classified as material do not underperform firms with poor performance on these same issues, suggesting investments in sustainability issues are at a minimum not value-destroying. Finally, firms with good performance on material issues and concurrently poor performance on immaterial issues perform the best. These results speak to the efficiency of firms’ sustainability investments, and also have implications for asset managers who have committed to the integration of sustainability factors in their capital allocation decisions.  Mozaffar Khan is a visiting Associate Professor at Harvard Business School. George Serafeim is the Jakurski Family Associate Professor of Business Administration at Harvard Business School. Aaron Yoon is a doctoral student at Harvard Business School. We are grateful for comments from seminar participants at National University of Singapore and Harvard Business School. We are grateful for financial support from the Division of Faculty Research and Development at Harvard Business School. George Serafeim has served on the Standards Council of SASB.  gserafeim@hbs.edu;
TET1 and hydroxymethylcytosine in transcription and DNA methylation fidelity

Enzymes catalysing the methylation of the 5-position of cytosine (mC) have essential roles in regulating gene expression and maintaining cellular identity. Recently, TET1 was found to hydroxylate the methyl group of mC, converting it to 5-hydroxymethyl cytosine (hmC). Here we show that TET1 binds throughout the genome of embryonic stem cells, with the majority of binding sites located at transcription start sites (TSSs) of CpG-rich promoters and within genes. The hmC modification is found in gene bodies and in contrast to mC is also enriched at CpG-rich TSSs. We provide evidence further that TET1 has a role in transcriptional repression. TET1 binds a significant proportion of Polycomb group target genes. Furthermore, TET1 associates and colocalizes with the SIN3A co-repressor complex. We propose that TET1 fine-tunes transcription, opposes aberrant DNA methylation at CpG-rich sequences and thereby contributes to the regulation of DNA methylation fidelity.
Analysis of basketball games using neural networks

Data mining is a technology in data analysis with rising application in sports. Basketball is one of most popular sports. Due to its dynamics, a large number of events happen during a game. Basketball statisticians have task to note as many of these events as possible, in order to provide their analysis. In this paper, we used data from the First B basketball league for men in Serbia, for seasons 2005/06, 2006/07, 2007/08, 2008/09 and 2009/2010. During these five seasons, total of 890 games were played. Data were collected for individual players, so it was necessary to adapt these in order to show statistics for a whole team. These data were analyzed using feedforward technique in neural networks, which is the most often used technique in analyzing nonlinear sports data. As a final result, we concluded that the most important elements in basketball are two-point shots under the hoop and defensive rebound, i.e. game “in paint”.
Probabilistic Inductive Logic Programming

Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.
Integrating object-oriented image analysis and decision tree algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR imagery

Traditional pixel-based classification methods yield poor results when applied to SAR imagery because of the presence of speckle and limited information in backscatter coefficients. A novel classification method, integrating polarimetric target decomposition, object-oriented image analysis, and decision tree algorithms, is proposed for the classification of polarimetric SAR data (PolSAR). The polarimetric target decomposition is aimed at extracting physical information related to the scattering mechanism of targets for the classification of scattering data. The main purposes of the object-oriented image analysis are delineating objects and extracting various spatial and textural features. The decision tree algorithm provides an efficient way to select features and create a decision tree for the classification. A comparison between the proposed method and the Wishart supervised classification was made. The overall accuracies of these two methods were 89.34% and 79.36%, respectively. The results show that the proposed method is an effective method for the classification of PolSAR data.
Performance measures of game controllers in a three-dimensional environment

Little work exists on the testing and evaluation of computer-game related input devices. This paper presents five new performance metrics and utilizes two tasks from the literature to quantify differences between input devices in constrained threedimensional environments, similar to “first-person”-genre games. The metrics are Mean Speed Variance, Mean Acceleration Variance, Percent View Moving, Target Leading Analysis, and Mean Time-to-Reacquire. All measures are continuous, as they evaluate movement during a trial. The tasks involved tracking a moving target for several seconds, with and without target acceleration. An evaluation between an X-Box gamepad and a standard PC mouse demonstrated the ability of the metrics to help reveal and explain performance differences between the devices. User/Machine Systems]: Human Factors; H.5.2 [ Evaluation Methodology.
Regularization Paths for Generalized Linear Models via Coordinate Descent.

We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ(1) (the lasso), ℓ(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.
Android smartphone vulnerabilities: A survey

Round the globe mobile devices like Smartphone, PDAs & tablets are playing an essential role in every person day to day lives. Various operating systems such as android, ios, blackberry etc provides a platform for smart devices. Google's Android is a one of the most popular and user friendly open source software platform for mobile devices. Along with its convenience people are likely to download and install malicious applications developed to misguide the user which will create a security gap to penetrate for the attacker. Hackers are inclined to discover and exploit the new vulnerabilities which will bring forth with the latest version of Android. In this paper, we concentrate on examining and understanding the vulnerabilities exist in android operating system. We will also suggest a metadata model which gives the information of all the related terms required for vulnerability assessment. Here, analyzing data is extracted from Open Source Vulnerability Database (OSVDB) and National Vulnerability Database (NVD).
An FPGA-based all-digital transmitter with 28-GHz time-interleaved delta-sigma modulation

An FPGA-based all-digital transmitter with 28-GHz ΔΣ-modulation is presented. For improving the operation frequency of ΔΣ modulator (DSM), a multi-channel look-ahead time-interleaved ΔΣ modulator is proposed. By implementing the proposed DSM in an FPGA, 28-GHz operation of DSM has been achieved. Furthermore, FPGA-based all-digital transmitter integrating the proposed DSM can satisfy the spectral mask of IEEE 802.11a WLAN standard in 5.2-GHz band. To the best of our knowledge, this is the first demonstration of an FPGA-based all-digital transmitter which can directly generate 5-GHz band WLAN signal.
Statistical analysis of repeated measures data using SAS procedures.

Mixed linear models were developed by animal breeders to evaluate genetic potential of bulls. Application of mixed models has recently spread to all areas of research, spurred by availability of advanced computer software. Previously, mixed model analyses were implemented by adapting fixed-effect methods to models with random effects. This imposed limitations on applicability because the covariance structure was not modeled. This is the case with PROC GLM in the SAS System. Recent versions of the SAS System include PROC MIXED. This procedure implements random effects in the statistical model and permits modeling the covariance structure of the data. Thereby, PROC MIXED can compute efficient estimates of fixed effects and valid standard errors of the estimates. Modeling the covariance structure is especially important for analysis of repeated measures data because measurements taken close in time are potentially more highly correlated than those taken far apart in time.