nils-herrmann/clean_scirepeval huggingface dataset:
Digital Media and Symptoms of Attention-Deficit/Hyperactivity Disorder in Adolescents.

In many regards, this trial represents the best of evidence-based quality improvement efforts—the improvement in health outcomes is often greater than what would have been expected from the individual components alone. An outcome of the trial by Wang et al is the proof that effective stroke interventions can be implemented in China where the burden of stroke is the highest in the world. Trials like this one leave a lasting legacy because the coaching and follow-up and the demonstration that data collection can lead to better outcomes with practice change will leave each of the intervention hospitals with a platform of good-quality stroke care and a mechanism to keep improving. Similar to the challenge of providing a novel drug to patients after a trial ends, a new dilemma that arises for the trialists is how to get the nonintervention hospitals up to the same level of care as the intervention hospitals. A major challenge is the sustainability of these kinds of interventions. Intensive quality improvement is just not practical for long periods; lower level embedded improvement needs to be built into the culture of a hospital, written into job descriptions, and identified as a deliverable in employment contracts to make continuous improvement possible and likely. In addition, the finer details of the intervention must be made available to others. A “teach the teacher” model may apply in distributing this kind of intervention widely. Overall, this randomized clinical trial by Wang et al has achieved its broad objective of both providing both stroke evidence and implanting practice change in 20 hospitals. The challenge now is to sustain that level of quality improvement at these hospitals, and expand this approach to other centers providing care for patients with acute stroke.
Combining link and content for community detection: a discriminative approach

In this paper, we consider the problem of combining link and content analysis for community detection from networked data, such as paper citation networks and Word Wide Web. Most existing approaches combine link and content information by a generative model that generates both links and contents via a shared set of community memberships. These generative models have some shortcomings in that they failed to consider additional factors that could affect the community memberships and isolate the contents that are irrelevant to community memberships. To explicitly address these shortcomings, we propose a discriminative model for combining the link and content analysis for community detection. First, we propose a conditional model for link analysis and in the model, we introduce hidden variables to explicitly model the popularity of nodes. Second, to alleviate the impact of irrelevant content attributes, we develop a discriminative model for content analysis. These two models are unified seamlessly via the community memberships. We present efficient algorithms to solve the related optimization problems based on bound optimization and alternating projection. Extensive experiments with benchmark data sets show that the proposed framework significantly outperforms the state-of-the-art approaches for combining link and content analysis for community detection.
Design and analysis of an origami continuum manipulation module with torsional strength

This paper presents an origami-inspired cable-driven continuum manipulator module that offers low-cost, low-volume deployment, light weight, and inherently safe human interaction and collaboration. Each module has a mass of around 110 g and integrates the actuation, sensing, and control sub-systems necessary for operation. The origami structure has 7.311 Nm/rad (0.128 Nm/degree) torsional stiffness while being capable of bending in two directions and changing arc-length down to a fully collapsed state. A maximum contraction of 35 mm and bending angle of 35.5 degrees were achieved with 45 mm arc length. The module is capable of passively supporting a 1-kg mass at its tip, or 4 additional serially connected modules, bending approximately 6 degrees in the worst case. We also show that we can actively compensate for external moments by pre-compressing or pre-bending the module. We utilize an inverse kinematic control scheme and use it for both open and closed loop control following a circular trajectory. Our results indicate that the module motion follows the desired trajectory with an RMS error of 0.681 mm in the horizontal (x-y) plane and 0.373 mm in the z-axis with closed-loop control. We also assembled two origami modules in series and drove them independently, demonstrating the proof-of-concept of a modular origami continuum manipulator.
An improved auto-tuning scheme for PID controllers.

An improved auto-tuning scheme is proposed for Ziegler-Nichols (ZN) tuned PID controllers (ZNPIDs), which usually provide excessively large overshoots, not tolerable in most of the situations, for high-order and nonlinear processes. To overcome this limitation ZNPIDs are upgraded by some easily interpretable heuristic rules through an online gain modifying factor defined on the instantaneous process states. This study is an extension of our earlier work [Mudi RK., Dey C. Lee TT. An improved auto-tuning scheme for PI controllers. ISA Trans 2008; 47: 45-52] to ZNPIDs, thereby making the scheme suitable for a wide range of processes and more generalized too. The proposed augmented ZNPID (AZNPID) is tested on various high-order linear and nonlinear dead-time processes with improved performance over ZNPID, refined ZNPID (RZNPID), and other schemes reported in the literature. Stability issues are addressed for linear processes. Robust performance of AZNPID is observed while changing its tunable parameters as well as the process dead-time. The proposed scheme is also implemented on a real time servo-based position control system.
Estimating within-study covariances in multivariate meta-analysis with multiple outcomes

Multivariate meta-analysis allows the joint synthesis of effect estimates based on multiple outcomes from multiple studies, accounting for the potential correlations among them. However, standard methods for multivariate meta-analysis for multiple outcomes are restricted to problems where the within-study correlation is known or where individual participant data are available. This paper proposes an approach to approximating the within-study covariances based on information about likely correlations between underlying outcomes. We developed methods for both continuous and dichotomous data and for combinations of the two types. An application to a meta-analysis of treatments for stroke illustrates the use of the approximated covariance in multivariate meta-analysis with correlated outcomes.
ELECTRONIC WORD-OF-MOUTH IN HOSPITALITY AND TOURISM MANAGEMENT

Interpersonal influence and word-of-mouth (WOM) are ranked the most important information source when a consumer is making a purchase decision. This influence may be especially important in the hospitality and tourism industry, whose intangible products are difficult to evaluate prior to their consumption. When WOM becomes digital, the largescale, anonymous, ephemeral nature of the Internet induces new ways of capturing, analyzing, interpreting, and managing online WOM. This paper describes online interpersonal influence, or eWOM, as a potentially cost-effective means for marketing hospitality and tourism, and discusses some of the nascent technological and ethical issues facing marketers as they seek to harness emerging eWOM technologies.
Implementing open innovation concept in social business

Social business is an emerging concept which requires additional research especially in terms of priority management tools that can be used. The paper aims to evaluate how closed and open innovation concepts are implemented by social enterprises in the emerging information economy. The paper also studies social enterprises from the point of view of joint efficiency and innovation development concept evaluation to define best practices used by social entrepreneurs nowadays. The paper provides comparative analysis of two types of social business models: the ones based on open innovation and the ones based on formal closed innovative process. Research question is to estimate how implementation of open innovation influences social businesses’ outcomes, and to define the way open innovation influences social enterprises’ efficiency.
Cyber crimes: Threats and protection

Information Technology has grown tremendously during the last two decades and became the main source of knowledge. The latest information and the current technology are available through the Internet making it the most valuable source of information to almost all people from the novice to the expert in all fields of knowledge. Yet with the usefulness and importance of the Internet, the misuse and the cyber crime become more evident. In this paper we try to explore some of these cyber crimes and the ways to protect from them. We discuss different types of cyber crimes namely: social engineering-phishing, email spoofing and pharming. We also discuss the ways to protect ourselves from such crimes with emphasis on biometrics.
Parking space detection from a radar based target list

The detection of an unoccupied parking spaces is a requirement of autonomous parking systems. This paper presents a radar based real-time algorithm, which detects, classifies, and evaluates parking spaces in a vehicle's immediate vicinity. In contrast to frequently used gridmap methods, our approach processes the radar data in a specially designed target list of 2D vectors. Consequently, quantization errors are avoided and computational burdens reduced. Experiments show that the proposed algorithm is suited for both parallel and perpendicular parking spaces in urban scenes.
Recognition of Affect, Judgment, and Appreciation in Text

The main task we address in our research is classification of text using fine-grained attitude labels. The developed @AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on the finegrained level (14 labels) was 62%, on the middle level (7 labels) – 71%, and on the top level (3 labels) – 88%.
Informing the Design of Spoken Conversational Search: Perspective Paper

We conducted a laboratory-based observational study where pairs of people performed search tasks communicating verbally. Examination of the discourse allowed commonly used interactions to be identified for Spoken Conversational Search (SCS). We compared the interactions to existing models of search behaviour. We find that SCS is more complex and interactive than traditional search. This work enhances our understanding of different search behaviours and proposes research opportunities for an audio-only search system. Future work will focus on creating models of search behaviour for SCS and evaluating these against actual SCS systems.
Orexins and Orexin Receptors: A Family of Hypothalamic Neuropeptides and G Protein-Coupled Receptors that Regulate Feeding Behavior

The hypothalamus plays a central role in the integrated control of feeding and energy homeostasis. We have identified two novel neuropeptides, both derived from the same precursor by proteolytic processing, that bind and activate two closely related (previously) orphan G protein-coupled receptors. These peptides, termed orexin-A and -B, have no significant structural similarities to known families of regulatory peptides. prepro-orexin mRNA and immunoreactive orexin-A are localized in neurons within and around the lateral and posterior hypothalamus in the adult rat brain. When administered centrally to rats, these peptides stimulate food consumption. prepro-orexin mRNA level is up-regulated upon fasting, suggesting a physiological role for the peptides as mediators in the central feedback mechanism that regulates feeding behavior.
SPICE Model of Memristor with Nonlinear Dopant Drift

A mathematical model of the prototype of memristor, manufactured in 2008 in Hewlett-Packard Labs, is described in the paper. It is shown that the hitherto published approaches to the modeling of boundary conditions need not conform with the requirements for the behavior of a practical circuit element. The described SPICE model of the memristor is thus constructed as an open model, enabling additional modifications of non-linear boundary conditions. Its functionality is illustrated on computer simulations.
Food region segmentation in meal images using touch points

We propose an interactive scheme for segmenting meal images for automated dietary assessment. A smartphone user photographs a meal and marks a few touch points on the resulting image. The segmentation algorithm initializes a set of food segments with the touch points, and grows them using local image features. We evaluate the algorithm with a data set consisting of 300 manually segmented meal images. The precision of segmentation is 0.87, compared with 0.70 for fully automatic segmentation. The results show that the precision of segmentation was significantly improved by incorporating minimal user intervention.
Paraphrase Identification Based on Weighted URAE, Unit Similarity and Context Correlation Feature

A deep learning model adaptive to both sentence-level and articlelevel paraphrase identification is proposed in this paper. It consists of pairwise unit similarity feature and semantic context correlation feature. In this model, sentences are represented by word and phrase embedding while articles are represented by sentence embedding. Those phrase and sentence embedding are learned from parse trees through Weighted Unfolding Recursive Autoencoders (WURAE), an unsupervised learning algorithm. Then, unit similarity matrix is calculated by matching the pairwise lists of embedding. It is used to extract the pairwise unit similarity feature through CNN and k-max pooling layers. In addition, semantic context correlation feature is taken into account, which is captured by the combination of CNN and LSTM. CNN layers learn collocation information between adjacent units while LSTM extracts the long-term dependency feature of the text based on the output of CNN. This model is experimented on a famous English sentence paraphrase corpus, MSRPC, and a Chinese article paraphrase corpus. The results show that the deep semantic feature of text could be extracted based on WURAE, unit similarity and context correlation feature. We release our code of WURAE, deep learning model for paraphrase identification and pre-trained phrase end sentence embedding data for use by the community.
Why Do Men Get More Attention? Exploring Factors Behind Success in an Online Design Community

Online platforms are an increasingly popular tool for people to produce, promote or sell their work. However recent studies indicate that social disparities and biases present in the real world might transfer to online platforms and could be exacerbated by seemingly harmless design choices on the site (e.g., recommendation systems or publicly visible success measures). In this paper we analyze an exclusive online community of teams of design professionals called Dribbble and investigate apparent differences in outcomes by gender. Overall, we find that men produce more work, and are able to show it to a larger audience thus receiving more likes. Some of this effect can be explained by the fact that women have different skills and design different images. Most importantly however, women and men position themselves differently in the Dribbble community. Our investigation of users’ position in the social network shows that women have more clustered and gender homophilous following relations, which leads them to have smaller and more closely knit social networks. Overall, our study demonstrates that looking behind the apparent patterns of gender inequalities in online markets with the help of social networks and product differentiation helps us to better understand gender differences in success and failure.
Structure guided texture inpainting through multi-scale patches and global optimization for image completion

Automatic image completion can provide convenient editing of consumer images. Most image completion methods find the best patch locally and then copy it to the hole region for texture inpainting. Since the best patch is fixed in size, it is hard to adapt these methods efficiently either to various patterns or to content synthesis. Meanwhile, salient image structures can be estimated and propagated to guide the texture inpainting process for more plausible results. This paper presents a novel image completion method using structure guided texture inpainting. We do not require any interactions to achieve automatic two-stage image completion. In the structure completion stage, the salient structures around the hole region are detected, and then the missing structure curves are completed with Euler spiral. The textures along the structure curves are completed through belief propagation. In the texture inpainting stage, we propose multi-scale patches and global optimization to inpaint the remaining texture in the hole regions guided by the completed structures. First, with defined patch sizes, the hole region is divided into lattice patches, making it possible for multiple patch sizes to render multiscale descriptions of this image. A multi-scale graph is then built for the hole region and formulated as a posterior probability model. Second, using a simulated annealing based Markov chain Monte Carlo method, an inference algorithm is designed to find a global optimization solution for the posterior probability model. The experiments show that our method can automatically complete the hole region and preserve plausible structure shapes of existing ones in various scenarios. The texture inpainting results are more convincing with guidance from the completed structures, and our method can guarantee and accelerate convergence of the global optimization.
Implicit and Explicit Stereotyping of Adolescents

Although adolescents are commonly assumed to be rebellious, risky and moody, two experiments demonstrate for the first time that these beliefs operate both explicitly and implicitly as stereotypes. In Experiment 1, participants (a) explicitly endorsed adolescent stereotypes and (b) implicitly associated adolescent stereotyped words more rapidly with the adolescent than the adult social category. Individual differences in the explicit endorsement of adolescent stereotypes predicted explicit perceptions of the rebelliousness of a 17-year-old but not a 71-year-old, although individual differences in implicit stereotyping did not. Identification with adults was associated with greater implicit stereotyping but not explicit stereotyping. In Experiment 2, subliminal exposure to adolescent stereotyped words increased subsequent perceptions of the rebelliousness of a 17-year-old but not a 71-year-old. Although individual differences in implicit adolescent stereotyping did not predict explicit evaluations of adolescents, stereotypes of adolescents nevertheless influenced explicit evaluations unconsciously and unintentionally.
12-to-1 bandwidth all-metal Vivaldi array element

The notch element is a classic example of a wideband antenna that functions very well as a stand-alone radiator and also as an element in wideband phased arrays [1, 2]. Also known as a Vivaldi Aerial or tapered-slot antenna, the Vivaldi has seen perhaps the most widespread use in ultra-high bandwidth applications (≫ 3:1 Bandwidth) of any type of element due to its robustness in design and range of manufacturability. Part of its popularity stems from the ease with which inexpensive printed-circuit board manufacturing can be used to create large arrays. It is also popular in high-power radar applications as well as ultra-wideband military applications, where all-metal designs are typically preferred. In this paper, a 12:1 bandwidth all-metal Vivaldi array element design is presented that can be easily machined from metal sheets and fed via standard SMA coax connectors. No soldering is required and assembly is simple and modular.
Changes in genital anatomy and microbiology in girls between age 6 and age 12 years: a longitudinal study.

To study changes in genital anatomy and occurrence of human papillomavirus and Gardnerella vaginalis in girls resulting from growth and development.



At age 11-12 years, an invitation was sent to 180 girls to attend a follow-up examination. All girls had previously participated in a study exploring anogenital anatomy and microbiology in children selected for non-abuse at age 5 and 6. The genital area was examined with a colposcope and microbiological samples for Gardnerella vaginalis (GV) and human papillomavirus (HPV) were collected. GV was identified by conventional criteria and HPV by a PCR method.



Thirty-one girls were examined twice, at mean age 5.7 and 12.0 years. At first examination all were pre-pubertal. At second examination 21 girls were B2/P2 or above. Significantly more girls had developed a structure called a fossa groove. A thick and redundant hymen with a tendency of folding outward was more common at the second examination. Two girls had GV and one girl had HPV-16 identified. Another girl was classified to have a deep notch and a probable transection in her hymen, and this girl reported a painful insertion of a tampon. All girls denied sexual activity.



The main genital finding in girls entering puberty is the hymen becoming thick and redundant with a tendency of folding out. In the study findings associated with sexual activity were discovered in two girls, and the possibility of alternative explanations is discussed.
Learning Simpler Language Models with the Delta Recurrent Neural Network Framework

Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks like language modeling. Existing architectures that address the issue are often complex and costly to train. The Delta Recurrent Neural Network (Delta-RNN) framework is a simple and highperforming design that unifies previously proposed gated neural models. The DeltaRNN models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. This requires hardly any more parameters than a classical simple recurrent network. In language modeling at the word and character levels, the models outperform popular complex architectures, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), and, when regularized, perform comparably to several state-of-the-art baselines. At the subword level, we find the Delta-RNN’s performance to be comparable to that of complex gated architectures.