nils-herrmann/clean_scirepeval huggingface dataset:
Association between physical activity and academic performance in Korean adolescent students

Recently, physical activity (PA) was found to improve cognitive and memory functions in the brain; however, no epidemiological studies have specifically investigated this phenomenon in the Korean adolescent student population. The purpose of this study was to investigate the effects of various types of PA undertaken at various frequencies, on the academic performance of Korean adolescent students.



A total of 75,066 adolescent students (39,612 males and 35,454 females) from the 7th to the 12th grades took part in the 5th Korea Youth Risk Behavior Web-based Survey (KYRBWS-V) project, conducted in 2009. Using data acquired by that survey, potential relations between PA and academic performance were explored in this current study through multivariate logistic regression analysis incorporating adjustment for covariate variables including age, body mass index, the parents' education level, and the income status of the family.



Compared with boys who did not regularly participate in any vigorous PA, those who did so 2, 3, or 4 times a week had greater odds of reporting an average or above-average academic performance. Compared with boys who did not participate in any moderate PA, those who did so 1, 2, 3, 4, or ≥5 times a week also had greater odds of reporting an average or above-average academic performance. Interestingly, when compared with boys who did not participate in any strengthening exercises, those undertaking strengthening exercises ≥5 times a week had lesser odds of reporting a below-average academic performance. Compared with girls who did not regularly participate in any vigorous PA, those who did so ≥5 times a week had greater odds of reporting an average or above-average academic performance. Compared with girls who did not participate in any moderate PA, those that did so 2 or 3 times a week had greater odds of reporting an average or above-average academic performance. Interestingly, when compared with girls who did not regularly participate in any strengthening exercises, those undertaking strengthening exercises ≥5 times a week had lesser odds of reporting a below-average academic performance.



Our analyses of the relevant data from the KYRBWS-V suggested that vigorous PA was positively correlated with academic performance in the case of boys, and moderate PA was positively correlated with academic performance in both boys and girls. However, strengthening exercises were not positively correlated with academic performance in boys or girls. Furthermore, when undertaken 5 or more times a week, vigorous PA in boys and strengthening exercises in both boys and girls were negatively correlated with academic performance. The results from this study are potentially relevant to the development of future education policies in Korean schools, particularly with regard to early intervention strategies designed to identify and counteract potential factors contributing to academic underachievement.
Transliteration for Resource-Scarce Languages

Today, parallel corpus-based systems dominate the transliteration landscape. But the resource-scarce languages do not enjoy the luxury of large parallel transliteration corpus. For these languages, rule-based transliteration is the only viable option. In this article, we show that by properly harnessing the monolingual resources in conjunction with manually created rule base, one can achieve reasonable transliteration performance. We achieve this performance by exploiting the power of Character Sequence Modeling (CSM), which requires only monolingual resources. We present the results of our rule-based system for Hindi to English, English to Hindi, and Persian to English transliteration tasks. We also perform extrinsic evaluation of transliteration systems in the context of Cross Lingual Information Retrieval. Another important contribution of our work is to explain the widely varying accuracy numbers reported in transliteration literature, in terms of the entropy of the language pairs and the datasets involved.
Potassium and sodium transporters of Pseudomonas aeruginosa regulate virulence to barley

We investigated the role of three uncharacterized cation transporters of Pseudomonas aeruginosa PAO1 as virulence factors for barley: PA1207, PA5021, and PA2647. PAO1 displayed reduced barley virulence with inactivated PA1207, PA5021, and PA2647 as well as with one known Na+/H+ antiporter, PA1820. Using the Escherichia coli LB2003 mutant lacking three K+ uptake systems, the expression of the PA5021 gene repressed LB2003 growth with low K+, but the strain acquired tolerance to high K+. In contrast, the expression of the PA1207 gene enhanced growth of LB2003 with low K+ but repressed its growth with high K+; therefore, the PA5021 protein exports K+, while the PA1207 protein imports K+. The PA5021 mutant of P. aeruginosa also showed impaired growth at 400 mM KCl and at 400 mM NaCl; therefore, the PA5021 protein may also export Na+. The loss of the PA5021 protein also decreased production of the virulence factor pyocyanin; corroborating this result, pyocyanin production decreased in wild-type PAO1 under high salinity. Whole-genome transcriptome analysis showed that PAO1 induced more genes in barley upon infection compared to the PA5021 mutant. Additionally, PAO1 infection induced water stress-related genes in barley, which suggests that barley may undergo water deficit upon infection by this pathogen.
Adhesive capsulitis: review of imaging findings, pathophysiology, clinical presentation, and treatment options

Adhesive capsulitis, commonly referred to as “frozen shoulder,” is a debilitating condition characterized by progressive pain and limited range of motion about the glenohumeral joint. It is a condition that typically affects middle-aged women, with some evidence for an association with endocrinological, rheumatological, and autoimmune disease states. Management tends to be conservative, as most cases resolve spontaneously, although a subset of patients progress to permanent disability. Conventional arthrographic findings include decreased capsular distension and volume of the axillary recess when compared with the normal glenohumeral joint, in spite of the fact that fluoroscopic visualization alone is rarely carried out today in favor of magnetic resonance imaging (MRI). MRI and MR arthrography (MRA) have, in recent years, allowed for the visualization of several characteristic signs seen with this condition, including thickening of the coracohumeral ligament, axillary pouch and rotator interval joint capsule, in addition to the obliteration of the subcoracoid fat triangle. Additional findings include T2 signal hyperintensity and post-contrast enhancement of the joint capsule. Similar changes are observable on ultrasound. However, the use of ultrasound is most clearly established for image-guided injection therapy. More aggressive therapies, including arthroscopic release and open capsulotomy, may be indicated for refractory disease, with arthroscopic procedures favored because of their less invasive nature and relatively high success rate.
Friends and neighbors on the Web

The Internet has become a rich and large repository of information about us as individuals. Anything from the links and text on a user's homepage to the mailing lists the user subscribes to are reflections of social interactions a user has in the real world. In this paper we devise techniques to mine this information in order to predict relationships between individuals. Further we show that some pieces of information are better indicators of social connections than others, and that these indicators vary between user populations and provide a glimpse into the social lives of individuals in different communities. Our techniques provide potential applications in automatically inferring real-world connections and discovering, labeling, and characterizing communities.
A task description language for robot control

Robot systems must achieve high level goals while remaining reactive to contingencies and new opportunities. This typically requires robot systems to coordinate concurrent activities, monitor the environment, and deal with exceptions. We have developed a new language to support such task-level control. The language, TDL, is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling. A compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library. This paper introduces TDL, describes the task treerepresentation that underlies the language, and presents some aspects of its implementation and use in an autonomous mobile robot.
Similarity Preserving Snippet-Based Visualization of Web Search Results

Internet users are very familiar with the results of a search query displayed as a ranked list of snippets. Each textual snippet shows a content summary of the referred document (or webpage) and a link to it. This display has many advantages, for example, it affords easy navigation and is straightforward to interpret. Nonetheless, any user of search engines could possibly report some experience of disappointment with this metaphor. Indeed, it has limitations in particular situations, as it fails to provide an overview of the document collection retrieved. Moreover, depending on the nature of the query for example, it may be too general, or ambiguous, or ill expressed the desired information may be poorly ranked, or results may contemplate varied topics. Several search tasks would be easier if users were shown an overview of the returned documents, organized so as to reflect how related they are, content wise. We propose a visualization technique to display the results of web queries aimed at overcoming such limitations. It combines the neighborhood preservation capability of multidimensional projections with the familiar snippet-based representation by employing a multidimensional projection to derive two-dimensional layouts of the query search results that preserve text similarity relations, or neighborhoods. Similarity is computed by applying the cosine similarity over a "bag-of-wordsâ' vector representation of collection built from the snippets. If the snippets are displayed directly according to the derived layout, they will overlap considerably, producing a poor visualization. We overcome this problem by defining an energy functional that considers both the overlapping among snippets and the preservation of the neighborhood structure as given in the projected layout. Minimizing this energy functional provides a neighborhood preserving two-dimensional arrangement of the textual snippets with minimum overlap. The resulting visualization conveys both a global view of the query results and visual groupings that reflect related results, as illustrated in several examples shown.
A silver inkjet printed ferrite NFC antenna

This paper presents a near field communication (NFC) antenna that is printed directly on a ferrite substrate using a silver inkjet printing process. Such a silver inkjet printed ferrite NFC antenna provides a minimum substrate thickness, a good operation on metal objects, and a small assembly effort. The NFC antenna performance is analysed by measurements based on the ISO/IEC standard 10373-6 for proximity identification cards test methods. For this, the antenna is connected to an NFC microchip. Measurements in a non-metal environment show that the ferrite antenna performs equally good a custom-built NFC antenna printed on photo paper substrate, despite additional losses in the ferrite substrate. In a metal environment the ferrite antenna clearly outperforms the photo paper antenna.
Multilevel Converters-A New Breed of Power Converters

AbstructMultilevel voltage source converters are emerging as a new breed of power converter options for high-power applications. The multilevel voltage source converters typically synthesize the staircase voltage wave from several levels of dc capacitor voltages. One of the major limitations of the multilevel converters is the voltage unbalance between different levels. The techniques to balance the voltage between different levels normally involve voltage clamping or capacitor charge control. There are several ways of implementing voltage balance in multilevel converters. Without considering the traditional magnetic coupled converters, this paper presents three recently developed multilevel voltage source converters: 1) diode-clamp, 2) flyingcapacitors, and 3) cascaded-inverters with separate dc sources. The operating principle, features, constraints, and potential applications of these converters will be discussed.
Learning Over Long Time Lags

The advantage of recurrent neural networks (RNNs) in learni ng dependencies between time-series data has distinguished RNNs from other deep learning models. Recent ly, many advances are proposed in this emerging field. However, there is a lack of comprehensive review on mem ory models in RNNs in the literature. This paper provides a fundamental review on RNNs and long short te rm memory (LSTM) model. Then, provides a surveys of recent advances in different memory enhancement s and learning techniques for capturing long term dependencies in RNNs.
PyNN: A Common Interface for Neuronal Network Simulators

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.
Vertex-Centric Graph Processing: The Good, the Bad, and the Ugly

We study distributed graph algorithms that adopt an iterati ve vertexcentric framework for graph processing, popularized by Goo gle’s Pregel system. Since then, there are several attempts to imp lement many graph algorithms in a vertex-centric framework, a s well as efforts to design optimization techniques for improving the efficiency. However, to the best of our knowledge, there has not been any systematic study to compare these vertex-centric i mplementations with their sequential counterparts. Our paper a dd esses this gap in two ways. (1) We analyze the computational complexity of such implementations with the notion of time-pro cessor product, and benchmark several vertex-centric graph algor ithms whether they perform more work with respect to their best-kn ow sequential solutions. (2) Employing the concept of balance d practical Pregel algorithms, we study if these implementations suffer from imbalanced workload and large number of iterations. Ou r findings illustrate that with the exception of Euler tour tre e algorithm, all other algorithms either perform asymptotically more work than their best-known sequential approach, or suffer from i mbalanced workload/ large number of iterations, or even both. We also emphasize on graph algorithms that are fundamentally diffic ult to be expressed in vertex-centric frameworks, and conclude by discussing the road ahead for distributed graph processing.
A Planning based Framework for Essay Generation

Generating an article automatically with computer program is a challenging task in artificial intelligence and natural language processing. In this paper, we target at essay generation, which takes as input a topic word in mind and generates an organized article under the theme of the topic. We follow the idea of text planning (Reiter and Dale, 1997) and develop an essay generation framework. The framework consists of three components, including topic understanding, sentence extraction and sentence reordering. For each component, we studied several statistical algorithms and empirically compared between them in terms of qualitative or quantitative analysis. Although we run experiments on Chinese corpus, the method is language independent and can be easily adapted to other language. We lay out the remaining challenges and suggest avenues for future research.
Kernel Belief Propagation

We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a reproducing kernel Hilbert space (RKHS), and message updates are simple linear operations in the RKHS. KBP makes none of the assumptions commonly required in classical BP algorithms: the variables need not arise from a finite domain or a Gaussian distribution, nor must their relations take any particular parametric form. Rather, the relations between variables are represented implicitly, and are learned nonparametrically from training data. KBP has the advantage that it may be used on any domain where kernels are defined (R, strings, groups), even where explicit parametric models are not known, or closed form expressions for the BP updates do not exist. The computational cost of message updates in KBP is polynomial in the training data size. We also propose a constant time approximate message update procedure by representing messages using a small number of basis functions. In experiments, we apply KBP to image denoising, depth prediction from still images, and protein configuration prediction: KBP is faster than competing classical and nonparametric approaches (by orders of magnitude, in some cases), while providing significantly more accurate results.
Resolution limit in community detection.

Detecting community structure is fundamental for uncovering the links between structure and function in complex networks and for practical applications in many disciplines such as biology and sociology. A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. This finding is confirmed through several examples, both in artificial and in real social, biological, and technological networks, where we show that modularity optimization indeed does not resolve a large number of modules. A check of the modules obtained through modularity optimization is thus necessary, and we provide here key elements for the assessment of the reliability of this community detection method.
Relational Data Mining in the Era of Big Data

The aim of this article is to synthetically describe a sample of distinct approaches and applications of Relational Data Mining, which address the issue of managing complex, and possibly big, amounts of data. Specifically, we report a brief review of the literature on Relational Data Mining in the fields of Spatial Data Mining, Process Mining, Network Data Analysis and Stream Data Mining, with an emphasis on the Italian research. For each field, we describe the milestones that have been reached, as well as the future research trends that are fuelled by the emergent ubiquity of Big Data.
Sea Ice Remote Sensing Using AMSR-E 89 GHz Channels

Recent progress in sea ice concentration remote sensing by satellite microwave radiometers has been stimulated by two developments: First, the new sensor AMSR-E offers spatial resolutions of approximately 6x4 km at 89 GHz, nearly three times the resolution of the standard sensor SSM/I at 85 GHz (15x13 km). Second, a new algorithm enables to estimate sea ice concentration from the channels near 90GHz, despite the enhanced atmospheric influence in these channels. This allows to fully exploit their horizontal resolution which is up to four times finer than the one of the channels near 19 and 37 GHz, the frequencies used by the most widespread algorithms for sea ice retrieval, the NASA-Team and Bootstrap algorithms. The ASI algorithm used combines a model for retrieving the sea ice concentration from SSM/I 85 GHz data proposed by Svendsen et al. [1987] with an ocean mask derived from the 18-, 23-, and 37-GHz AMSR-E data using weather filters. During two ship campaigns, the correlation of ASI, NASA-Team 2 and Bootstrap algorithms ice concentrations with bridge observations were 0.80, 0.79 and 0.81, respectively. Systematic differences over the complete AMSR-E period (2002-2006) between ASI and NASA-Team 2 are below −2±8.8%, and between ASI and Bootstrap 1.7±10.8%. Among the geophysical implications of the ASI algorithm are: (1) Its higher spatial resolution allows to better estimate crucial variables in numerical atmospheric and ocean models, e.g. the heat flux between ocean and atmosphere, especially near coastlines and in polynyas. (2) It provides an additional time series of ice area and extent for climate studies.
Advanced GPU Raycasting

Modern GPUs offer a degree of programmability that opens up a wide field of applications far beyond processing millions of triangles at ever increasing speed. Raycasting is one of these applications that can make heavy use of the built-in features of todays graphics cards. With the possibilities offered by this technology, there is a lot of room for new techniques that do not simply convert existing algorithms to the GPU, but use the very strengths of this architecture to create more realistic images at interactive frame rates. This paper presents an approach to hardware based raycasting in the fragment shader of a shader model 3 compatible graphics card that not only allows for both orthogonal and perspective projection, but enables the user to move the viewpoint into the dataset for fly-through applications like virtual endoscopy. This hardware-based approach can also be used to correctly intersect the rendered dataset with normal OpenGL geometry, allowing arbitrary 3D-meshes, pointers or grids to be rendered in the same scene. The last section deals with the biggest problem of GPUbased raycasting the limited amount of available video RAM and how it can be circumvented by applying a cached blocking scheme that loads only blocks of interest into the memory.
A Study on Image Annotation Techniques

With the development of mobile device, large amount of digital images are generated every day. The requirements of effective indexing and searching image are growing rapidly. Image annotation is an effective way for content based image retrieval. This paper gives a review of different categories for image annotation. The paper also gives a brief overview of different methods for image annotation under SemiAutomatic and Automatic annotations. Manual annotation is a costly and time consuming work, especially for mobile device.