nils-herrmann/clean_scirepeval huggingface dataset:
The repurposed social brain

Human social intelligence depends on a diverse array of perceptual, cognitive, and motivational capacities. Some of these capacities depend on neural systems that may have evolved through modification of ancestral systems with non-social or more limited social functions (evolutionary repurposing). Social intelligence, in turn, enables new forms of repurposing within the lifetime of an individual (cultural and instrumental repurposing), which entail innovating over and exploiting pre-existing circuitry to meet problems our brains did not evolve to solve. Considering these repurposing processes can provide insight into the computations that brain regions contribute to social information processing, generate testable predictions that usefully constrain social neuroscience theory, and reveal biologically imposed constraints on cultural inventions and our ability to respond beneficially to contemporary challenges.
Learning to Rank Microblog Posts for Real-Time Ad-Hoc Search

Microblogging websites have emerged to the center of information production and diffusion, on which people can get useful information from other users’ microblog posts. In the era of Big Data, we are overwhelmed by the large amount of microblog posts. To make good use of these informative data, an effective search tool is required specialized for microblog posts. However, it is not trivial to do microblog search due to the following reasons: 1) microblog posts are noisy and time-sensitive rendering general information retrieval models ineffective. 2) Conventional IR models are not designed to consider microblog-specific features. In this paper, we propose to utilize learning to rank model for microblog search. We combine content-based, microblog-specific and temporal features into learning to rank models, which are found to model microblog posts effectively. To study the performance of learning to rank models, we evaluate our models using tweet data set provided by TERC 2011 and TREC 2012 microblogs track with the comparison of three stateof-the-art information retrieval baselines, vector space model, language model, BM25 model. Extensive experimental studies demonstrate the effectiveness of learning to rank models and the usefulness to integrate microblog-specific and temporal information for microblog search task.
A DAPTON : Composable , Demand-Driven Incremental Computation ( Extended version )

Many researchers have proposed programming languages that support incremental computation (IC), which allows programs to be efficiently re-executed after a small change to the input. However, existing implementations of such languages have two important drawbacks. First, recomputation is oblivious to specific demands on the program output; that is, if a program input changes, all dependencies will be recomputed, even if an observer no longer requires certain outputs. Second, programs are made incremental as a unit, with little or no support for reusing results outside of their original context, e.g., when reordered. To address these problems, we present λ ic , a core calculus that applies a demand-driven semantics to incremental computation, tracking changes in a hierarchical fashion in a novel demanded computation graph. λ ic also formalizes an explicit separation between inner, incremental computations and outer observers. This combination ensures λ ic programs only recompute computations as demanded by observers, and allows inner computations to be reused more liberally. We present ADAPTON, an OCaml library implementing λ ic . We evaluated ADAPTON on a range of benchmarks, and found that it provides reliable speedups, and in many cases dramatically outperforms state-of-the-art IC approaches.
Developmental Consequences of Fetal Exposure to Drugs: What We Know and What We Still Must Learn

Most drugs of abuse easily cross the placenta and can affect fetal brain development. In utero exposures to drugs thus can have long-lasting implications for brain structure and function. These effects on the developing nervous system, before homeostatic regulatory mechanisms are properly calibrated, often differ from their effects on mature systems. In this review, we describe current knowledge on how alcohol, nicotine, cocaine, amphetamine, Ecstasy, and opiates (among other drugs) produce alterations in neurodevelopmental trajectory. We focus both on animal models and available clinical and imaging data from cross-sectional and longitudinal human studies. Early studies of fetal exposures focused on classic teratological methods that are insufficient for revealing more subtle effects that are nevertheless very behaviorally relevant. Modern mechanistic approaches have informed us greatly as to how to potentially ameliorate the induced deficits in brain formation and function, but conclude that better delineation of sensitive periods, dose–response relationships, and long-term longitudinal studies assessing future risk of offspring to exhibit learning disabilities, mental health disorders, and limited neural adaptations are crucial to limit the societal impact of these exposures.
Fibrinogen-induced perivascular microglial clustering is required for the development of axonal damage in neuroinflammation

Blood-brain barrier disruption, microglial activation and neurodegeneration are hallmarks of multiple sclerosis. However, the initial triggers that activate innate immune responses and their role in axonal damage remain unknown. Here we show that the blood protein fibrinogen induces rapid microglial responses toward the vasculature and is required for axonal damage in neuroinflammation. Using in vivo two-photon microscopy, we demonstrate that microglia form perivascular clusters before myelin loss or paralysis onset and that, of the plasma proteins, fibrinogen specifically induces rapid and sustained microglial responses in vivo. Fibrinogen leakage correlates with areas of axonal damage and induces reactive oxygen species release in microglia. Blocking fibrin formation with anticoagulant treatment or genetically eliminating the fibrinogen binding motif recognized by the microglial integrin receptor CD11b/CD18 inhibits perivascular microglial clustering and axonal damage. Thus, early and progressive perivascular microglial clustering triggered by fibrinogen leakage upon blood-brain barrier disruption contributes to axonal damage in neuroinflammatory disease.
Estimation and Tracking: Principles, Techniques, and Software [Reviews and Abstracts]

his is an excellent book The preface tells us that “[tlhe text presents the material from a second semestef graduate level course on Estimation offered in the Department of Electrical and Systems Engineering [the] main goal of this course is to convey the knowledge necessary for the evaluation and design of state estimators that operate in a stochastic environment” To a great extent, the material is extracted from class lectures, but it does not suffer from the cryptic style commonly associated with lecture notes
Changes in physico-chemical properties, polyphenol compounds and antiradical activity during development and ripening of maoluang (Antidesma bunius L. Spreng) fruits.

Influence of fruit development and ripening on the changes in physico-chemical properties, antiradical activity and the accumulation of polyphenolic compounds were investigated in Maoluang fruits. Total phenolics content (TP) was assayed according to the Folin-Ciocalteu method, and accounted for 19.60-8.66 mg GAE/g f.w. The TP gradually decreased from the immature to the over ripe stages. However, the total anthocyanin content (TA) showed the highest content at the over ripe stage, with an average value of 141.94 mg/100 g f.w. The antiradical activity (AA) of methanolic extracts from Maoluang fruits during development and ripening were determined with DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging. The highest AA was observed at the immature stage accompanied by the highest content of gallic acid and TP. Polyphenols were quantified by HPLC. The level of procyanidin B2, procyanidin B1, (+)-catechin, (–)-epicatechin, rutin and tran-resveratrol as the main polyphenol compounds, increased during fruit development and ripening. Other phenolic acids such as gallic, caffeic, and ellagic acids significantly decreased (p < 0.05) during fruit development and ripening. At over ripe stage, Maoluang possess the highest antioxidants. Thus, the over ripe stage would be the appropriate time to harvest when taking nutrition into consideration. This existing published information provides a helpful daily diet guide and useful guidance for industrial utilization of Maoluang fruits.
The Relationship Between Self-Esteem and Sexual Self-Concept in People With Physical-Motor Disabilities

Self-esteem is the value that the individuals give themselves, and sexual self-concept is also a part of individuality or sexual-self. Impairment or disability exists not only in the physical body of disabled people but also in their attitudes. Negative attitudes affect the mental health of disabled people, causing them to have lower self-esteem.



This study aimed to examine the relationship between self-esteem and sexual self-concept in people with physical-motor disabilities.



This cross-sectional study was conducted on 200 random samples with physical-motor disabilities covered by Isfahan Welfare Organization in 2013. Data collection instruments were the Persian Eysenck self-esteem questionnaire, and five domains (sexual anxiety, sexual self-efficacy, sexual self-esteem, sexual fear and sexual depression) of the Persian multidimensional sexual self-concept questionnaire. Because of incomplete filling of the questionnaires, the data of 183 people were analyzed by the SPSS 16.0 software. Data were analyzed using the t-test, Man-Whitney and Kruskal-Wallis tests and Spearman correlation coefficient.



The mean age was 36.88 ± 8.94 years for women and 37.80 ± 10.13 for men. The mean scores of self-esteem among women and men were 15.80 ± 3.08 and 16.2 ± 2.90, respectively and there was no statistically significance difference. Comparison of the mean scores of sexual anxiety, sexual self-efficacy, sexual self-esteem, sexual fear and sexual depression among men and women showed that women scored higher than men in all domains. This difference was statistically significant in other domains except the sexual self-esteem (14.92 ± 3.61 vs. 13.56 ± 4.52) (P < 0.05). The Kruskal-Wallis test showed that except for sexual anxiety and sexual self-esteem, there was a statistical difference between other domains of people's sexual self-concept and degree of disability (P < 0.05). Moreover, Spearman coefficient showed that there was only a correlation between men's sexual anxiety, sexual self-esteem and sexual self-efficacy with their self-esteem. This correlation was positive in sexual anxiety and negative in two other domains.



Lack of difference in self-esteem of disabled people in different degrees of disability and in both men and women suggests that disabled people should not be presumed to have low self-esteem, and their different aspects of life should be attended to, just like others. Furthermore, studies should be designed and implemented based on psychological, social and environmental factors that can help disabled people to promote their positive sexual self-concept through marriage, and reduce their negative self-concept.
A simple but exact and efficient algorithm for complex root isolation

We present a new exact subdivision algorithm CEVAL for isolating the complex roots of a square-free polynomial in any given box. It is a generalization of a previous real root isolation algorithm called EVAL. Under suitable conditions, our approach is applicable for general analytic functions. CEVAL is based on the simple Bolzano Principle and is easy to implement exactly. Preliminary experiments have shown its competitiveness.
 We further show that, for the "benchmark problem" of isolating all roots of a square-free polynomial with integer coefficients, the asymptotic complexity of both algorithms EVAL and CEVAL matches (up a logarithmic term) that of more sophisticated real root isolation methods which are based on Descartes' Rule of Signs, Continued Fraction or Sturm sequence. In particular, we show that the tree size of EVAL matches that of other algorithms. Our analysis is based on a novel technique called Δ-clusters from which we expect to see further applications.
A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks

The power prediction for photovoltaic (PV) power plants has significant importance for their grid connection. Due to PV power’s periodicity and non-stationary characteristics, traditional power prediction methods based on linear or time series models are no longer applicable. This paper presents a method combining the advantages of the wavelet decomposition (WD) and artificial neural network (ANN) to solve this problem. With the ability of ANN to address nonlinear relationships, theoretical solar irradiance and meteorological variables are chosen as the input of the hybrid model based on WD and ANN. The output power of the PV plant is decomposed using WD to separated useful information from disturbances. The ANNs are used to build the models of the decomposed PV output power. Finally, the outputs of the ANN models are reconstructed into the forecasted PV plant power. The presented method is compared with the traditional forecasting method based on ANN. The results shows that the method described in this paper needs less calculation time and has better forecasting precision.
Multi-Resolution Planning for Earthmoving

We suggest that planning for automated earthmoving operations such as digging a foundation or leveling a mound of soil, be treated at multiple levels. In a system that we have developed, a coarse-level planner is used to tessellate the volume to be excavated into smaller pieces that are sequenced in order to complete the task efficiently. Each of the smaller volumes is treated with a refined planner that selects digging actions based on constraint optimization over the space of prototypical digging actions. We discuss planners and the associated representations for two types of earthmoving machines: an excavator backhoe and a wheel loader. Experimental results from a full-scale automated excavator and simulated wheel loader are presented .
A rotated ellipsoidal angle density function improves estimation of foliage inclination distributions in forest canopies

The ‘ellipsoidal distribution’, in which angles are assumed to be distributed parallel to the surface of an oblate or prolate ellipsoid, has been widely used to describe the leaf angle distribution (LAD) of plant canopies. This ellipsoidal function is constrained to show a probability density of zero at an inclination angle of zero; however, actual LADs commonly show a peak probability density at zero, a pattern consistent with functional models of plant leaf display. A ‘rotated ellipsoidal distribution’ is described here, which geometrically corresponds to an ellipsoid in which small surface elements are rotated normal to the surface. Empirical LADs from canopy and understory species in an old-growth coniferous forest were used to compare the two models. In every case the rotated ellipsoidal function provided a better description of empirical data than did the non-rotated function, while retaining only a single parameter. The ratio of G-statistics for goodness of fit for the two functions ranged from 1.03 to 3.88. The improved fit is due to the fact that the rotated function always shows a probability density greater than zero at inclination angles of zero, can show a mode at zero, and more accurately characterizes the overall shape of empirical distributions.
A Survey of Digital Television Broadcast Transmission Techniques

This paper is a survey of the transmission techniques used in digital television (TV) standards worldwide. With the increase in the demand for High-Definition (HD) TV, video-on-demand and mobile TV services, there was a real need for more bandwidth-efficient, flawless and crisp video quality, which motivated the migration from analogue to digital broadcasting. In this paper we present a brief history of the development of TV and then we survey the transmission technology used in different digital terrestrial, satellite, cable and mobile TV standards in different parts of the world. First, we present the Digital Video Broadcasting standards developed in Europe for terrestrial (DVB-T/T2), for satellite (DVB-S/S2), for cable (DVB-C) and for hand-held transmission (DVB-H). We then describe the Advanced Television System Committee standards developed in the USA both for terrestrial (ATSC) and for hand-held transmission (ATSC-M/H). We continue by describing the Integrated Services Digital Broadcasting standards developed in Japan for Terrestrial (ISDB-T) and Satellite (ISDB-S) transmission and then present the International System for Digital Television (ISDTV), which was developed in Brazil by adopteding the ISDB-T physical layer architecture. Following the ISDTV, we describe the Digital Terrestrial television Multimedia Broadcast (DTMB) standard developed in China. Finally, as a design example, we highlight the physical layer implementation of the DVB-T2 standard.
Development of software to design passive filters for EMI suppression in SiC DC fed motor drives

This paper proposes an EMI filter design software which can serve as an aid to the designer to quickly arrive at optimal filter sizes based on off-line measurement data or simulation results. The software covers different operating conditions-such as: different switching devices, different types of switching techniques, different load conditions and layout of the test setup. The proposed software design works for both silicon based and WBG based power converters.
Vertical and Transverse Management with Transpalatal Arches in an Adult with Class III Malocclusion

The transpalatal arch might be one of the most common intraoral auxiliary fixed appliances used in orthodontics in order to provide dental anchorage. The aim of the present case report is to describe a case in which an adult patient with a tendency to class III, palatal compression, and bilateral posterior crossbite was treated with double transpalatal bars in order to control the torque of both the first and the second molars. Double transpalatal arches on both first and second maxillary molars are a successful appliance in order to control the posterior sectors and improve the torsion of the molars. They allow the professional to gain overbite instead of losing it as may happen with other techniques and avoid enlarging of Wilson curve, obtaining a more stable occlusion without the need for extra help from bone anchorage.
Extending quality metrics to full luminance range images

Many quality metrics take as input gamma corrected images and assume that pixel code values are scaled perceptually uniform. Although this is a valid assumption for darker displays operating in the luminance range typical for CRT displays (from 0.1 to 80 cd/m), it is no longer true for much brighter LCD displays (typically up to 500 cd/m), plasma displays (small regions up to 1000 cd/m) and HDR displays (up to 3000 cd/m). The distortions that are barely visible on dark displays become clearly noticeable when shown on much brighter displays. To estimate quality of images shown on bright displays, we propose a straightforward extension to the popular quality metrics, such as PSNR and SSIM, that makes them capable of handling all luminance levels visible to the human eye without altering their results for typical CRT display luminance levels. Such extended quality metrics can be used to estimate quality of high dynamic range (HDR) images as well as account for display brightness.
Stream-based joint exploration-exploitation active learning

Learning from streams of evolving and unbounded data is an important problem, for example in visual surveillance or internet scale data. For such large and evolving real-world data, exhaustive supervision is impractical, particularly so when the full space of classes is not known in advance therefore joint class discovery (exploration) and boundary learning (exploitation) becomes critical. Active learning has shown promise in jointly optimising exploration-exploitation with minimal human supervision. However, existing active learning methods either rely on heuristic multi-criteria weighting or are limited to batch processing. In this paper, we present a new unified framework for joint exploration-exploitation active learning in streams without any heuristic weighting. Extensive evaluation on classification of various image and surveillance video datasets demonstrates the superiority of our framework over existing methods.
Locally linear embedding (LLE) for MRI based Alzheimer's disease classification

Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer's disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. Specifically, we used the unsupervised learning algorithm of local linear embedding (LLE) to transform multivariate MRI data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions, while also utilizing the global nonlinear data structure. The embedded brain features were then used to train a classifier for predicting future conversion to AD based on a baseline MRI. We tested the approach on 413 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had baseline MRI scans and complete clinical follow-ups over 3 years with the following diagnoses: cognitive normal (CN; n=137), stable mild cognitive impairment (s-MCI; n=93), MCI converters to AD (c-MCI, n=97), and AD (n=86). We found that classifications using embedded MRI features generally outperformed (p<0.05) classifications using the original features directly. Moreover, the improvement from LLE was not limited to a particular classifier but worked equally well for regularized logistic regressions, support vector machines, and linear discriminant analysis. Most strikingly, using LLE significantly improved (p=0.007) predictions of MCI subjects who converted to AD and those who remained stable (accuracy/sensitivity/specificity: =0.68/0.80/0.56). In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: =0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvement in predicting conversion to AD in MCI could have important implications for health management and for powering therapeutic trials by targeting non-demented subjects who later convert to AD.
Economics of Internet of Things (IoT): An Information Market Approach

Internet of things (IoT) has been proposed to be a new paradigm of connecting devices and providing services to various applications, e.g., transportation, energy, smart city, and healthcare. In this paper, we focus on an important issue, i.e., economics of IoT, that can have a great impact to the success of IoT applications. In particular, we adopt and present the information economics approach with its applications in IoT. We first review existing economic models developed for IoT services. Then, we outline two important topics of information economics which are pertinent to IoT, i.e., the value of information and information good pricing. Finally, we propose a game theoretic model to study the price competition of IoT sensing services. Some outlooks on future research directions of applying information economics to IoT are discussed.