nils-herrmann/clean_scirepeval huggingface dataset:
Antenatal corticosteroids prior to 24 weeks' gestation and neonatal outcome of extremely low birth weight infants.

We sought to determine if antenatal corticosteroid treatment administered prior to 24 weeks' gestation influences neonatal morbidity and mortality in extremely low-birth-weight infants. A retrospective review was performed of all singleton pregnancies treated with one complete course of antenatal corticosteroids prior to 24 weeks' gestation and delivered between 23(0)/(7) and 25(6)/(7) weeks. These infants were compared with similar gestational-age controls. There were no differences in gender, race, birth weight, and gestational age between the groups. Infants exposed to antenatal corticosteroids had lower mortality (29.3% versus 62.9%, P = 0.001) and grade 3 or 4 intraventricular hemorrhage (IVH; 16.7% versus 36%, P < 0.05; relative risk [RR]: 2.16). Grade 3 and 4 IVH was associated with significantly lower survival probability as compared with no IVH or grade 1 and 2 IVH (P < 0.001, RR: 10.6, 95% confidence interval [CI]: 4.4 to 25.6). Antenatal steroid exposure was associated with a 62% decrease in the hazard rate compare with those who did not receive antenatal steroids after adjusting for IVH grade (Cox proportional hazard model, hazard ratio 0.38, 95% CI: 0.152 to 0.957, P = 0.04). The rates of premature rupture of membranes and chorioamnionitis were higher for infants exposed to antenatal corticosteroids. Exposure to a single course of antenatal corticosteroids prior to 24 weeks' gestation was associated with reduction of the risk of severe IVH and neonatal mortality for extremely low-birth-weight infants.
Semantic motion segmentation for urban dynamic scene understanding

A mount of recent researches on scene parsing and semantic labeling, while few focus on obtaining joint semantic motion labeling. In this paper, we propose an approach to infer both the object class and motion status for each pixel of images. First, we extract and match sparse image features to estimate ego-motion between two consecutive stereo images, the result of feature points grouping is used to segment moving object in U-disparity map. Second, a Fully Convolutional Neural Network is employed for semantic segmentation. Moreover, semantic cues are utilized to remove pixels have no potential to be moved in motion mask. Finally, we use a fully connected CRF to integrate motion into semantic segmentation. To validate the effectiveness of the proposed algorithm, we present experimental results with KITTI stereo images that contain moving objects.
Dynamics of large-scale electrophysiological networks: A technical review

For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity.
Motion Coordination for VTOL Unmanned Aerial Vehicles

Motion Coordination for VTOL Unmanned Aerial Vehicles develops new control design techniques for the distributed coordination of a team of autonomous unmanned aerial vehicles. In particular, it provides new control design approaches for the attitude synchronization of a formation of rigid body systems. In addition, by integrating new control design techniques with some concepts from nonlinear control theory and multi-agent systems, it presents a new theoretical framework for the formation control of a class of under-actuated aerial vehicles capable of vertical take-off and landing.
Simulating patho-realistic ultrasound images using deep generative networks with adversarial learning

Ultrasound imaging makes use of backscattering of waves during their interaction with scatterers present in biological tissues. Simulation of synthetic ultrasound images is a challenging problem on account of inability to accurately model various factors of which some include intra-/inter scanline interference, transducer to surface coupling, artifacts on transducer elements, inhomogeneous shadowing and nonlinear attenuation. Current approaches typically solve wave space equations making them computationally expensive and slow to operate. We propose a generative adversarial network (GAN) inspired approach for fast simulation of patho-realistic ultrasound images. We apply the framework to intravascular ultrasound (IVUS) simulation. A stage 0 simulation performed using pseudo B-mode ultrasound image simulator yields speckle mapping of a digitally defined phantom. The stage I GAN subsequently refines them to preserve tissue specific speckle intensities. The stage II GAN further refines them to generate high resolution images with patho-realistic speckle profiles. We evaluate patho-realism of simulated images with a visual Turing test indicating an equivocal confusion in discriminating simulated from real. We also quantify the shift in tissue specific intensity distributions of the real and simulated images to prove their similarity.
Field effects of social media platforms on information-sharing continuance: Do reach and richness matter?

Based on the Field Theory, this study postulates that the cognitive processes involved in making decisions to share information on social media platforms could be dynamically affected by network features and the contextual environment. The field effect is exerted by the reach and richness of network features, which virtually form a psychological pressure on one’s perception of the sharing situation. A research model is developed, in which the effects of extrinsic and intrinsic motivators on information-sharing continuance are moderated by the network features of social media platforms. A global sample from content contributors in two major social media platform contexts, experience-socialization (ES) platforms (N = 568) and intelligence-proliferation (IP) platforms (N = 653), were collected through the participatory research method. By using partial least-square analysis, the moderating effects of network features on cognitive-sharing processes under the two contexts were confirmed. For contributors on ES platforms, network features negatively moderate community identification and perceived enjoyment toward information sharing. By contrast, for contributors on IP platforms, network features negatively moderate the effects of perceived usefulness and altruistic tendencies on their intention to share, but self-efficacy for sharing is positively induced. The conceptualization of network features and refined knowledge about the situational and contextual effects of social media platforms are useful for further studies on social behaviors and may ultimately benefit platform providers in their attempts to promote information-sharing continuance. ã 2016 Elsevier B.V. All rights reserved.
Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models

Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case study using a version of the Enron e-mail corpus dataset demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy and supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analyzing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under -edge differential privacy, and then use likelihood based inference for missing data and Markov chain Monte Carlo techniques to fit exponential-family random graph models to the generated synthetic networks.
The Garnet user interface development environment : a proposal

The Garnet project aims to create a set of tools that will help user interface designers create, modify and maintain highly-interactive, graphical, direct manipulation user interfaces. These tools will form a "User Interface Development Environment" (UBDE), which is sometimes called a 'User Interface Management System" (UIMS). Garnet takes a new approach to UIDEs by concentrating on a particular class of programs: those whose primary focus is creating and editing graphical objects. Garnet is composed of six major parts: an object-oriented graphics package, a constraint system, encapsulated input device handlers called "interactors," a user interface tool kit, user interface construction tools, and a "graphical editor shell" to help build editor-style programs. This document presents an overview of the approach that we propose to take for the Garnet project. This research was sponsored by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976, Amendment 20, under contract F33615-87-C-1499, monitored by the Avionics Laboratory, Air Force Wright Aeronautical Laboratories, Aeronautical Systems Division (AFSC), Wright-Patterson AFB, Ohio 45433-6543. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US Government. Brad A. Myers The Garnet UIDE Table of
Study on Distinct Approaches for Sentiment Analysis

Now-a-days many researchers work on mining a content posted in natural language at different forums, blogs or social networking sites. Sentiment analysis is rapidly expanding topic with various applications. Previously a person collect response from any relatives previous to procuring an object, but today look is different, now person get reviews of many people on all sides of world. Blogs, e-commerce sites data consists number of implications, that expressing user opinions about specific object. Such data is pre-processed then classified into classes as positive, negative and irrelevant. Sentiment analysis allows us to determine view of public or general users feeling about any object. Two global techniques are used: Supervised Machine-Learning and Unsupervised machine-learning methods. In unsupervised learning use a lexicon with words scored for polarity values such as neutral, positive or negative. Whereas supervised methods require a training set of texts with manually assigned polarity values. This suggest one direction is make use of Fuzzy logic for sentiment analysis which may improve analysis results.
Environmental Impact Assessment and End-of-Life Treatment Policy Analysis for Li-Ion Batteries and Ni-MH Batteries

Based on Life Cycle Assessment (LCA) and Eco-indicator 99 method, a LCA model was applied to conduct environmental impact and end-of-life treatment policy analysis for secondary batteries. This model evaluated the cycle, recycle and waste treatment stages of secondary batteries. Nickel-Metal Hydride (Ni-MH) batteries and Lithium ion (Li-ion) batteries were chosen as the typical secondary batteries in this study. Through this research, the following results were found: (1) A basic number of cycles should be defined. A minimum cycle number of 200 would result in an obvious decline of environmental loads for both battery types. Batteries with high energy density and long life expectancy have small environmental loads. Products and technology that help increase energy density and life expectancy should be encouraged. (2) Secondary batteries should be sorted out from municipal garbage. Meanwhile, different types of discarded batteries should be treated separately under policies and regulations. (3) The incineration rate has obvious impact on the Eco-indicator points of Nickel-Metal Hydride (Ni-MH) batteries. The influence of recycle rate on Lithium ion (Li-ion) batteries is more obvious. These findings indicate that recycling is the most promising direction for reducing secondary batteries' environmental loads. The model proposed here can be used to evaluate environmental loads of other secondary batteries and it can be useful for proposing policies and countermeasures to reduce the environmental impact of secondary batteries.
Efficient system design space exploration using machine learning techniques

Computer manufacturers spend a huge amount of time, resources, and money in designing new systems and newer configurations, and their ability to reduce costs, charge competitive prices and gain market share depends on how good these systems perform. In this work, we develop predictive models for estimating the performance of systems by using performance numbers from only a small fraction of the overall design space. Specifically, we first develop three models, two based on artificial neural networks and another based on linear regression. Using these models, we analyze the published Standard Performance Evaluation Corporation (SPEC) benchmark results and show that by using the performance numbers of only 2% and 5% of the machines in the design space, we can estimate the performance of all the systems within 9.1% and 4.6% on average, respectively. Then, we show that the performance of future systems can be estimated with less than 2.2% error rate on average by using the data of systems from a previous year. We believe that these tools can accelerate the design space exploration significantly and aid in reducing the corresponding research/development cost and time-to-market.
To what extent does the anxiety scale of the Four-Dimensional Symptom Questionnaire (4DSQ) detect specific types of anxiety disorder in primary care? A psychometric study

Anxiety scales may help primary care physicians to detect specific anxiety disorders among the many emotionally distressed patients presenting in primary care. The anxiety scale of the Four-Dimensional Symptom Questionnaire (4DSQ) consists of an admixture of symptoms of specific anxiety disorders. The research questions were: (1) Is the anxiety scale unidimensional or multidimensional? (2) To what extent does the anxiety scale detect specific DSM-IV anxiety disorders? (3) Which cut-off points are suitable to rule out or to rule in (which) anxiety disorders?



We analyzed 5 primary care datasets with standardized psychiatric diagnoses and 4DSQ scores. Unidimensionality was assessed through confirmatory factor analysis (CFA). We examined mean scores and anxiety score distributions per disorder. Receiver operating characteristic (ROC) analysis was used to determine optimal cut-off points.



Total n was 969. CFA supported unidimensionality. The anxiety scale performed slightly better in detecting patients with panic disorder, agoraphobia, social phobia, obsessive compulsive disorder (OCD) and post traumatic stress disorder (PTSD) than patients with generalized anxiety disorder (GAD) and specific phobia. ROC-analysis suggested that ≥4 was the optimal cut-off point to rule out and ≥10 the cut-off point to rule in anxiety disorders.



The 4DSQ anxiety scale measures a common trait of pathological anxiety that is characteristic of anxiety disorders, in particular panic disorder, agoraphobia, social phobia, OCD and PTSD. The anxiety score detects the latter anxiety disorders to a slightly greater extent than GAD and specific phobia, without being able to distinguish between the different anxiety disorder types. The cut-off points ≥4 and ≥10 can be used to separate distressed patients in three groups with a relatively low, moderate and high probability of having one or more anxiety disorders.
Design and Implementation of an Algorithm to Enhance Cloud Security

Cloud computing concept has been envisioned as architecture of the next generation for Information Technology (IT) enterprise. The Cloud computing idea offers with dynamic scalable resources provisioned as examine on the Internet. It allows access to remote computing services and users only have to pay for what they want to use, when they want to use it. But the security of the information which is stored in the cloud is the major issue for a cloud user. Cloud computing has been flourishing in past years because of its ability to provide users with on-demand, flexible, reliable, and low-cost services. With more and more cloud applications being available, data security becomes an important issue to the cloud. In order to make sure security of the information at cloud data storage end, a design and implementation of an algorithm to enhance cloud security is proposed. With a concept, where the proposed algorithm (PA) combines features of two other existing algorithms named Ceaser cipher and Attribute based cryptography (ABC). In this research work, text information are encrypting using &quot;Caesar Cipher&quot; then produced cipher text again encrypted by using proposed algorithm (PA) with the help of private key of 128 bits. And in the last step of encryption process, based on ABC, attribute related to cipher text is stored along with cipher text
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking

In this paper, we propose the first higher frame rate video dataset (called Need for Speed - NfS) and benchmark for visual object tracking. The dataset consists of 100 videos (380K frames) captured with now commonly available higher frame rate (240 FPS) cameras from real world scenarios. All frames are annotated with axis aligned bounding boxes and all sequences are manually labelled with nine visual attributes - such as occlusion, fast motion, background clutter, etc. Our benchmark provides an extensive evaluation of many recent and state-of-the-art trackers on higher frame rate sequences. We ranked each of these trackers according to their tracking accuracy and real-time performance. One of our surprising conclusions is that at higher frame rates, simple trackers such as correlation filters outperform complex methods based on deep networks. This suggests that for practical applications (such as in robotics or embedded vision), one needs to carefully tradeoff bandwidth constraints associated with higher frame rate acquisition, computational costs of real-time analysis, and the required application accuracy. Our dataset and benchmark allows for the first time (to our knowledge) systematic exploration of such issues, and will be made available to allow for further research in this space.
Variation in Soil and Forest Floor Characteristics Along Gradients of Ericaceous, Evergreen Shrub Cover in the Southern Appalachians

Ericaceous shrubs can influence soil properties in many ecosystems. In this study, we examined how soil and forest floor properties vary among sites with different ericaceous evergreen shrub basal area in the southern Appalachian mountains. We randomly located plots along transects that included open understories and understories with varying amounts of Rhododendron maximum (rosebay rhododendron) and Kalmia latifolia (mountain laurel) at three sites. The three sites were a mid-elevation ridge, a low-elevation cove, and a high-elevation southwest-facing slope. Basal area of R. maximum was more correlated with soil properties of the forest floor than was K. latifolia. Increasing R. maximum basal area was correlated with increasing mass of lower quality litter and humus as indicated by higher C:N ratios. Moreover, this correlation supports our prediction that understory evergreen shrubs may have considerable effect on forest floor resource heterogeneity in mature stands.  Vegetation is one of the primary factors contributing to soil genesis (Boettcher and Kalisz 1990). Many studies have shown the effects of individual tree species on soil chemical and physical properties (Zinke 1962, Challinor 1968, Chastain et al. 2006, Boerner and Koslowsky 1989, Boettcher and Kalisz 1990, Pelletier et al. 1999). Woody species may affect soil properties by redistributing nutrients within the rooting zone (Boettcher and Kalisz 1990) and by the synthesis and input of organic material in the form of root exudates and decomposing litter (Boerner and Koslowsky 1989). Additionally, litter quality may influence decomposition and nutrient turnover rates. For example, litter of species with higher lignin content has slower decomposition rates (Hobbie et al. 2006). Ericaceous plants, in particular, influence soil properties by reducing soil enzyme activities and slowing nutrient cycling (Bloom and Mallik 2006, Chastain et al. 2006, Joanisse et al. 2007). This influence is primarily a result of litter quality and a large concentration of polyphenolic compounds (Wurzburger and Hendrick 2007). These phenolic compounds often bind with organic materials in the soil preventing or slowing their decomposition (Joanisse et al. 2007, Wurzberger and Hendrick 2007), reducing rates of nutrient mineralization (Straker 1996, Northup et al. 1998). Many ericaceous plants influence nutrient availability in cold-temperate or boreal regions that are nitrogen limited (Nilsson and Wardle 2005); however, ericaceous vegetation can also alter nutrient cycling in warm *email address: jhorton@unca.edu **Present address: Department of Biology, Appalachian State University, Boone, North Carolina 28608. Present address: Adirondack Ecosystem Research Center, State University of New York, and College of Environmental Science and Forestry, Syracuse, New York 12852. Received February 26, 2008;  2009. CASTANEA 74(4): 340–352. DECEMBER 2009
Phenomenology and carnival production chain

The book “The Visible and The Invisible” (Merleau-Ponty 1971), first published in French in 1964, is an achievement in itself of the phenomenology of Maurice MerleauPonty, where the writing is gradually constructed as a process shared with the reader. But, in Brazil, many other achievements derived from this work. Is was read by Mario Pedrosa—a brazilian intellectual involved in the Rio de Janeiro artistic field, specially the Grupo Frente, that in the 1950s seeked a new language of concrete art forms.2 This article raises the hypothesis that there would be a greater consequence of this intellectual influence over brazilian neoconcrete works, such as Hélio Oiticica’s—part of this influence would have spread beyond erudite circles of the visual arts, especially in the carnival of Samba Schools of the city of Rio de Janeiro. The relationship that Oiticica established with the dancers of Mangueira is widely noted as quoted by the artist himself in many