nils-herrmann/clean_scirepeval huggingface dataset:
Quantifying training intensity distribution in elite endurance athletes: is there evidence for an "optimal" distribution?

This study was designed to quantify the daily distribution of training intensity in a group of well-trained junior cross-country skiers and compare the results of three different methods of training intensity quantification. Eleven male athletes performed treadmill tests to exhaustion to determine heart rate and VO2 corresponding to ventilatory thresholds (VT1, VT2), maximal oxygen consumption (VO2max), and maximal heart rate. VT1 and VT2 were used to delineate three intensity zones. During the same time period, all training sessions (N=384, 37 strength training, 347 endurance) performed over 32 consecutive days were quantified using continuous heart rate registration and session Rating of Perceived Exertion (RPE). In addition, a subset of 60 consecutive training sessions was quantified using blood lactate measurements. Intensity distribution across endurance training sessions (n=318) was similar when based on heart rate analysis (75+/-3%, zone 1; 8+/-3%, zone 2; 17+/-4%, zone 3) or session RPE (76+/-4%, zone 1; 6+/-5%, zone 2; 18+/-7%, zone 3). Similarly, from measurements of 60 consecutive sessions, 71% were performed with <or=2.0 mM blood lactate, 7% between 2 and 4 mM, and 22% with over 4 mM (mean=9.5+/-2.8 mM). In this group of nationally competitive junior skiers, training was organized after a polarized pattern, with most sessions performed clearly below (about 75%) or with substantial periods above (15-20%) the lactate accommodation zone, which is bounded by VT1 and VT2. The pattern quantified here is similar to that reported in observational studies of elite endurance athletes across several sports. It appears that elite endurance athletes train surprisingly little at the lactate threshold intensity.
What is Large in Large-Scale? A Taxonomy of Scale for Agile Software Development

Positive experience of agile development methods in smaller projects has created interest in the applicability of such methods in larger scale projects. However, there is a lack of conceptual clarity regarding what large-scale agile software development is. This inhibits effective collaboration and progress in the research area. In this paper, we suggest a taxonomy of scale for agile software development projects that has the potential to clarify what topics researchers are studying and ease discussion of research priorities.
Context awareness in health care: A review

Health care systems will integrate new computing paradigms in the coming years. Context-awareness computing is a research field which often refers to health care as an interesting and rich area of application.



Through a survey of the research literature, we intended to derive an objective view of the actual dynamism of context awareness in health care, and to identify strengths and weaknesses in this field.



After discussing definitions of context, we proposed a simple framework to analyse and characterize the use of context through three main axes. We then focused on context-awareness computing and reported on the main teams working in this area. We described some of the context-awareness projects in health care. A deeper analysis of the hospital-based projects demonstrated the gap between recommendations expressed for modelling context awareness and the actual use in a prototype. Finally, we identified pitfalls encountered in this area of research.



A number of opportunities remain for this evolving field of research. We found relatively few groups with such a specific focus. As yet there is no consensus as to the most appropriate models or attributes to include in context awareness. We conclude that a greater understanding of which aspects of context are important in a health care setting is required; the inherent sociotechnical nature of context-aware applications in health care; and the need to draw on a number of disciplines to conduct this research.
Lifted Probabilistic Inference with Counting Formulas

Lifted inference algorithms exploit repeated structure in probabilistic models to answer queries efficiently. Previous work such as de Salvo Braz et al.’s first-order variable elimination (FOVE) has focused on the sharing of potentials across interchangeable random variables. In this paper, we also exploit interchangeability within individual potentials by introducing counting formulas, which indicate how many of the random variables in a set have each possible value. We present a new lifted inference algorithm, C-FOVE, that not only handles counting formulas in its input, but also creates counting formulas for use in intermediate potentials. C-FOVE can be described succinctly in terms of six operators, along with heuristics for when to apply them. Because counting formulas capture dependencies among large numbers of variables compactly, C-FOVE achieves asymptotic speed improvements compared to FOVE.
Political , economic and environmental impacts of biofuels : A review

Current energy policies address environmental issues including environmentally friendly technologies to increase energy supplies and encourage cleaner, more efficient energy use, and address air pollution, greenhouse effect, global warming, and climate change. The biofuel policy aims to promote the use in transport of fuels made from biomass, as well as other renewable fuels. Biofuels provide the prospect of new economic opportunities for people in rural areas in oil importer and developing countries. The central policy of biofuel concerns job creation, greater efficiency in the general business environment, and protection of the environment. Projections are important tools for long-term planning and policy settings. Renewable energy sources that use indigenous resources have the potential to provide energy services with zero or almost zero emissions of both air pollutants and greenhouse gases. Biofuels are expected to reduce dependence on imported petroleum with associated political and economic vulnerability, reduce greenhouse gas emissions and other pollutants, and revitalize the economy by increasing demand and prices for agricultural products.
MODEL PREDICTIVE CONTROL OF BUCK-BOOST DC-DCCONVERTERFOR CONSTANT VOLTAGE APLLICATIONS

This paper proposes a Model Predictive Control methodology for a non-inverting Buck-Boost DC-DC converter for its efficient control. PID and MPC control strategies are simulated for the control of Buck-Boost converter and its performance is compared using MATLAB Simulink model. MPC shows better performance compared to PID controller. Output follows reference voltage more accurately showing that MPC can handle the dynamics of the system efficiently. The proposed methodology can be used for constant voltage applications. The control strategy can be implemented using a Field Programmable Gate Array (FPGA).
Internet and Social Media Addiction

This book is a fruitful discussion of the Internet and social media addiction in the digital era. It includes informative and impressive facts about this issue. It clarifies the consequences of the extreme usage of Internet and social media in four educative chapters. The publisher of this book, “ReferencePoint Press”, introduced the “Compact Research Series” to think deeply with focusing on 3 types of information namely objective single author narratives, opinion-based primary source quotations as well as facts and statistics. The addiction series consists of five books examine the risk and challenges of different addictions, from synthetic drugs and heroin to gambling and Internet addiction. Andrea C. Nakaya, the author of this book has a Master’s degree in Communication from San Diego State University. She has been working as a freelance author for almost a decade.
Comorbid substance use disorders in schizophrenia: A latent class approach

Schizophrenia is a complex psychiatric disorder with a characteristic disease course and heterogeneous etiology. While substance use disorders and a family history of psychosis have individually been identified as risk factors for schizophrenia, it is less well understood if and how these factors are related. To address this deficiency, we examined the relationship between substance use disorders and family history of psychosis in a sample of 1219 unrelated patients with schizophrenia. The lifetime rate of substance use disorders in this sample was 50%, and 30% had a family history of psychosis. Latent class mixture modeling identified three distinct patient subgroups: (1) individuals with low probability of substance use disorders; (2) patients with drug and alcohol abuse, but no symptoms of dependence; and (3) patients with substance dependence. Substance use was related to being male, to a more severe disease course, and more acute symptoms at assessment, but not to an earlier age of onset of schizophrenia or a specific pattern of positive and negative symptoms. Furthermore, substance use in schizophrenia was not related to a family history of psychosis. The results suggest that substance use in schizophrenia is an independent risk factor for disease severity and onset.
Recent advances in robust optimization: An overview

This paper provides an overview of developments in robust optimization since 2007. It seeks to give a representative picture of the research topics most explored in recent years, highlight common themes in the investigations of independent research teams and highlight the contributions of rising as well as established researchers both to the theory of robust optimization and its practice. With respect to the theory of robust optimization, this paper reviews recent results on the cases without and with recourse, i.e., the static and dynamic settings, as well as the connection with stochastic optimization and risk theory, the concept of distributionally robust optimization, and findings in robust nonlinear optimization. With respect to the practice of robust optimization, we consider a broad spectrum of applications, in particular inventory and logistics, finance, revenue management, but also queueing networks, machine learning, energy systems and the public good. Key developments in the period from 2007 to present include: (i) an extensive body of work on robust decision-making under uncertainty with uncertain distributions, i.e., ‘‘robustifying’’ stochastic optimization, (ii) a greater connection with decision sciences by linking uncertainty sets to risk theory, (iii) further results on nonlinear optimization and sequential decision-making and (iv) besides more work on established families of examples such as robust inventory and revenue management, the addition to the robust optimization literature of new application areas, especially energy systems and the public good.
Machine Learning for Biometrics

Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. We focus on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, we hope to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems.
Testing the technology acceptance model for evaluating healthcare professionals' intention to use an adverse event reporting system.

Many healthcare organizations have implemented adverse event reporting systems in the hope of learning from experience to prevent adverse events and medical errors. However, a number of these applications have failed or not been implemented as predicted.



This study presents an extended technology acceptance model that integrates variables connoting trust and management support into the model to investigate what determines acceptance of adverse event reporting systems by healthcare professionals.



The proposed model was empirically tested using data collected from a survey in the hospital environment. A confirmatory factor analysis was performed to examine the reliability and validity of the measurement model, and a structural equation modeling technique was used to evaluate the causal model.



The results indicated that perceived usefulness, perceived ease of use, subjective norm, and trust had a significant effect on a professional's intention to use an adverse event reporting system. Among them, subjective norm had the most contribution (total effect). Perceived ease of use and subjective norm also had a direct effect on perceived usefulness and trust, respectively. Management support had a direct effect on perceived usefulness, perceived ease of use, and subjective norm.



The proposed model provides a means to understand what factors determine the behavioral intention of healthcare professionals to use an adverse event reporting system and how this may affect future use. In addition, understanding the factors contributing to behavioral intent may potentially be used in advance of system development to predict reporting systems acceptance.
Direction Awareness in Citation Recommendation

Literature search is an important part of academic research. The increase in the number of published papers each year makes manual search inefficient, hence, automatic methods must be devised. Unfortunately, traditional search engines use keyword-based approaches to solve the search problem which are prone to ambiguity and synonymity. This paper focuses on the problem of extending a set of references using the citation relations between the documents. In particular, we introduce the class of direction-aware algorithms which weight the importance of incoming and outgoing edges of the citation graph differently based on user preferences. Using such an algorithm, the user can easily focus her search toward recent developments or traditional papers. We present two direction-aware algorithms and show that they are better suited at solving the problem at hand than state-of-theart recommendation methods. One of these algorithms is currently deployed in a publicly available web-service called theadvisor.
Ordinary Differential Equations in Cancer Biology

Ordinary differential equations (ODEs) provide a classical framework to model the dynamics of biological systems, given temporal experimental data. Qualitative analysis of the ODE model can lead to further biological insight and deeper understanding compared to traditional experiments alone. Simulation of the model under various perturbations can generate novel hypotheses and motivate the design of new experiments. This short paper will provide an overview of the ODE modeling framework, and present examples of how ODEs can be used to address problems in cancer biology.
Text Mining

Many text mining algorithms and applications require the availability of large text corpora and certain statistics-based annotations. To ensure comparability of results a standardized corpus building process is required. Particularly noteworthy are all pre-processing procedures as they are crucial for the quality of the resulting data stock. This quality can be estimated by both evaluating the corpus building process and by statistical quality measurements on the corpus. Some of these approaches are described using the example of the Leipzig Corpora Collection.
Effects of a health information system data quality intervention on concordance in Mozambique: time-series analyses from 2009–2012

We assessed the effects of a three-year national-level, ministry-led health information system (HIS) data quality intervention and identified associated health facility factors.



Monthly summary HIS data concordance between a gold standard data quality audit and routine HIS data was assessed in 26 health facilities in Sofala Province, Mozambique across four indicators (outpatient consults, institutional births, first antenatal care visits, and third dose of diphtheria, pertussis, and tetanus vaccination) and five levels of health system data aggregation (daily facility paper registers, monthly paper facility reports, monthly paper district reports, monthly electronic district reports, and monthly electronic provincial reports) through retrospective yearly audits conducted July-August 2010-2013. We used mixed-effects linear models to quantify changes in data quality over time and associated health system determinants.



Median concordance increased from 56.3% during the baseline period (2009-2010) to 87.5% during 2012-2013. Concordance improved by 1.0% (confidence interval [CI]: 0.60, 1.5) per month during the intervention period of 2010-2011 and 1.6% (CI: 0.89, 2.2) per month from 2011-2012. No significant improvements were observed from 2009-2010 (during baseline period) or 2012-2013. Facilities with more technical staff (aβ: 0.71; CI: 0.14, 1.3), more first antenatal care visits (aβ: 3.3; CI: 0.43, 6.2), and fewer clinic beds (aβ: -0.94; CI: -1.7, -0.20) showed more improvements. Compared to facilities with no stock-outs, facilities with five essential drugs stocked out had 51.7% (CI: -64.8 -38.6) lower data concordance.



A data quality intervention was associated with significant improvements in health information system data concordance across public-sector health facilities in rural and urban Mozambique. Concordance was higher at those facilities with more human resources for health and was associated with fewer clinic-level stock-outs of essential medicines. Increased investments should be made in data audit and feedback activities alongside targeted efforts to improve HIS data in low- and middle-income countries.
Fractional Fourier transform estimation of simple randomly sampled signals

Random sampling as a special kind of nonuniform sampling has been used as a digital alias-free signal processing method in analog-to-digital conversion. This paper presents the fractional Fourier transform estimation of nonstationary signals based on the simple random sampling. The spectrum estimation is proved to be unbiased. The variance of it is calculated to show the estimation effect. The effects of sampling jitters and observation errors on performance of the fractional spectrum estimation are analyzed. Among them, the sampling jitters introduce bias to the estimation. The bias can be compensated by the new defined fractional characteristic function. All of the analysis results are simulated and verified in the last numerical experiments.
Convolutional Neural Networks over Tree Structures for Programming Language Processing

Feb. 15, 2016, 2‐3:30 pm Chair: Xiaobai Liu, San Diego State University (SDSU) Oral Presentations 920: On the Depth of Deep Neural Networks: A Theoretical View. Shizhao Sun, Wei Chen, Liwei Wang, Xiaoguang Liu, Tie‐Yan Liu 1229: How Important Is Weight Symmetry in Backpropagation? Qianli Liao, Joel Z. Leibo, Tomaso Poggio 1769: Deep Learning with S‐shaped Rectified Linear Activation Units. Xiaojie Jin, Chunyan Xu, Jiashi Feng, Yunchao Wei, Junjun Xiong, Shuicheng Yan 1142: Learning Step Size Controllers for Robust Neural Network Training. Christian Daniel, Jonathan Taylor, Sebastian Nowozin
The Long-Short Story of Movie Description

Generating descriptions for videos has many applications including assisting blind people and human-robot interaction. The recent advances in image captioning as well as the release of large-scale movie description datasets such as MPII-MD [28] allow to study this task in more depth. Many of the proposed methods for image captioning rely on pre-trained object classifier CNNs and Long-Short Term Memory recurrent networks (LSTMs) for generating descriptions. While image description focuses on objects, we argue that it is important to distinguish verbs, objects, and places in the challenging setting of movie description. In this work we show how to learn robust visual classifiers from the weak annotations of the sentence descriptions. Based on these visual classifiers we learn how to generate a description using an LSTM. We explore different design choices to build and train the LSTM and achieve the best performance to date on the challenging MPII-MD dataset. We compare and analyze our approach and prior work along various dimensions to better understand the key challenges of the movie description task.
Some economics of private digital currency

This paper reviews some recent developments in digital currency, focusing on platform-sponsored currencies such as Facebook Credits. In a model of platform management, we find that it will not likely be profitable for such currencies to expand to become fully convertible competitors to state-sponsored currencies.  The views here are those of the authors and no responsibility for them should be attributed to the Bank of Canada. We thank participants at the NBER Economics of Digitization Conference, Warren Weber and Glen Weyl for helpful comments on an earlier draft of this paper. Please send any comments to joshua.gans@gmail.com.
Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control

This paper presents a class of linear predictors for nonlinear controlled dynamical systems. The basic idea is to lift (or embed) the nonlinear dynamics into a higher dimensional space where its evolution is approximately linear. In an uncontrolled setting, this procedure amounts to numerical approximations of the Koopman operator associated to the nonlinear dynamics. In this work, we extend the Koopman operator to controlled dynamical systems and apply the Extended Dynamic Mode Decomposition (EDMD) to compute a finite-dimensional approximation of the operator in such a way that this approximation has the form of a linear controlled dynamical system. In numerical examples, the linear predictors obtained in this way exhibit a performance superior to existing linear predictors such as those based on local linearization or the so called Carleman linearization. Importantly, the procedure to construct these linear predictors is completely data-driven and extremely simple – it boils down to a nonlinear transformation of the data (the lifting) and a linear least squares problem in the lifted space that can be readily solved for large data sets. These linear predictors can be readily used to design controllers for the nonlinear dynamical system using linear controller design methodologies. We focus in particular on model predictive control (MPC) and show that MPC controllers designed in this way enjoy computational complexity of the underlying optimization problem comparable to that of MPC for a linear dynamical system with the same number of control inputs and the same dimension of the state-space. Importantly, linear inequality constraints on the state and control inputs as well as nonlinear constraints on the state can be imposed in a linear fashion in the proposed MPC scheme. Similarly, cost functions nonlinear in the state variable can be handled in a linear fashion. We treat both the full-state measurement case and the input-output case, as well as systems with disturbances / noise. Numerical examples (including a high-dimensional nonlinear PDE control) demonstrate the approach with the source code available online2.