nils-herrmann/clean_scirepeval huggingface dataset:
Learning text representation using recurrent convolutional neural network with highway layers

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bidirectional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the input. The experiment shows that our model outperforms common neural network models (CNN, RNN, Bi-RNN) on a sentiment analysis task. Besides, the analysis of how sequence length influences the RCNN with highway layers shows that our model could learn good representation for the long text.
Sudden forest canopy collapse corresponding with extreme drought and heat in a mediterranean-type eucalypt forest in southwestern Australia

Drought and heat-induced forest dieback and mortality are emerging global concerns. Although Mediterranean-type forest (MTF) ecosystems are considered to be resilient to drought and other disturbances, we observed a sudden and unprecedented forest collapse in a MTF in Western Australia corresponding with record dry and heat conditions in 2010/2011. An aerial survey and subsequent field investigation were undertaken to examine: the incidence and severity of canopy dieback and stem mortality, associations between canopy health and stand-related factors as well as tree species response. Canopy mortality was found to be concentrated in distinct patches, representing 1.5 % of the aerial sample (1,350 ha). Within these patches, 74 % of all measured stems (>1 cm DBHOB) had dying or recently killed crowns, leading to 26 % stem mortality six months following the collapse. Patches of canopy collapse were more densely stocked with the dominant species, Eucalyptus marginata, and lacked the prominent midstorey species Banksia grandis, compared to the surrounding forest. A differential response to the disturbance was observed among co-occurring tree species, which suggests contrasting strategies for coping with extreme water stress. These results suggest that MTFs, once thought to be resilient to climate change, are susceptible to sudden and severe forest collapse when key thresholds have been reached.
Complex regional pain syndrome: a recent update

Complex regional pain syndrome (CRPS) is a debilitating condition affecting the limbs that can be induced by surgery or trauma. This condition can complicate recovery and impair one's functional and psychological well-being. The wide variety of terminology loosely used to describe CRPS in the past has led to misdiagnosis of this condition, resulting in poor evidence-base regarding the treatment modalities available and their impact. The aim of this review is to report on the recent progress in the understanding of the epidemiology, pathophysiology and treatment of CRPS and to discuss novel approaches in treating this condition.
"It's all about acceptance": A qualitative study exploring a model of positive body image for people with spinal cord injury.

Using modified constructivist grounded theory, the purpose of the present study was to explore positive body image experiences in people with spinal cord injury. Nine participants (five women, four men) varying in age (21-63 years), type of injury (C3-T7; complete and incomplete), and years post-injury (4-36 years) were recruited. The following main categories were found: body acceptance, body appreciation and gratitude, social support, functional gains, independence, media literacy, broadly conceptualizing beauty, inner positivity influencing outer demeanour, finding others who have a positive body image, unconditional acceptance from others, religion/spirituality, listening to and taking care of the body, managing secondary complications, minimizing pain, and respect. Interestingly, there was consistency in positive body image characteristics reported in this study with those found in previous research, demonstrating universality of positive body image. However, unique characteristics (e.g., resilience, functional gains, independence) were also reported demonstrating the importance of exploring positive body image in diverse groups.
A weighted adaptive least-squares finite element method for the Poisson-Boltzmann equation

The finite element methodology has become a standard framework for approximating the solution to the Poisson-Boltzmann equation in many biological applications. In this article, we examine the numerical efficacy of least-squares finite element methods for the linearized form of the equations. In particular, we highlight the utility of a first-order form, noting optimality, control of the flux variables, and flexibility in the formulation, including the choice of elements. We explore the impact of weighting and the choice of elements on conditioning and adaptive refinement. In a series of numerical experiments, we compare the finite element methods when applied to the problem of computing the solvation free energy for realistic molecules of varying size.
Efficient Support for Complex Numbers in Java

One glaring weakness of Java for numerical programming is its lack of support for complex numbers. Simply creating a Complex number class leads to poor performance relative to Fortran. We show in this paper, however, that the combination of such aComplex class and a compiler that understands its semantics does indeed lead to Fortran-like performance. This performance gain is achieved while leaving the Java language completely unchanged and maintaining full compatibility with existing Java Virtual Machines . We quantify the effectiveness of our approach through experiments with linear algebra, electromagnetics, and computational fluid-dynamics kernels.
Narrow Band Internet of Things

In this paper, we review the background and state-of-the-art of the narrow-band Internet of Things (NB-IoT). We first introduce NB-IoT general background, development history, and standardization. Then, we present NB-IoT features through the review of current national and international studies on NB-IoT technology, where we focus on basic theories and key technologies, i.e., connection count analysis theory, delay analysis theory, coverage enhancement mechanism, ultra-low power consumption technology, and coupling relationship between signaling and data. Subsequently, we compare several performances of NB-IoT and other wireless and mobile communication technologies in aspects of latency, security, availability, data transmission rate, energy consumption, spectral efficiency, and coverage area. Moreover, we analyze five intelligent applications of NB-IoT, including smart cities, smart buildings, intelligent environment monitoring, intelligent user services, and smart metering. Finally, we summarize security requirements of NB-IoT, which need to be solved urgently. These discussions aim to provide a comprehensive overview of NB-IoT, which can help readers to understand clearly the scientific problems and future research directions of NB-IoT.
Heritage and early history of the boundary element method

This article explores the rich heritage of the boundary element method (BEM) by examining its mathematical foundation from the potential theory, boundary value problems, Green’s functions, Green’s identities, to Fredholm integral equations. The 18th to 20th century mathematicians, whose contributions were key to the theoretical development, are honored with short biographies. The origin of the numerical implementation of boundary integral equations can be traced to the 1960s, when the electronic computers had become available. The full emergence of the numerical technique known as the boundary element method occurred in the late 1970s. This article reviews the early history of the boundary element method up to the late 1970s.
Video Super-Resolution using Temporal Fusion Generative Adversarial Network

Super-Resolution(SR) aims at recovering a highresolution image or video from its low-resolution version. It has many applications such as medical imaging, satellite imaging, and face recognition. In this project, we present a generative adversarial network (GAN) for video superresolution. To our knowledge, GAN has been applied on single image super-resolution and demonstrated promising results, however, there has not been any literacy on video super-resolution using GAN. Furthermore, videos have rich information on the temporal domain which could be beneficial to the task of super-resolution. Our target is to build a video super-resolution framework using Generative Adversarial Network with temporal information fusion. The effectiveness of this approach is demonstrated through experiments. Comparisons are made against state-of-art perframe based SRGAN method.[4]
Detection of fatigue crack anomaly: a symbolic dynamics approach

This paper presents a novel method for early detection of fatigue crack anomaly in complex mechanical structures, which is built upon the concepts of symbolic dynamics and finite state machines. The experimental apparatus, on which the crack detection method is tested, is a multi-degree of freedom mass-beam structure excited by oscillatory motion of two vibrators. The evolution of fatigue crack at one or more of the three failure sites causes slow variations in the natural frequencies of the mechanical structure, which are detected at an early stage from the time series data of displacement sensor signals. The proposed anomaly detection method has been validated by comparison with existing pattern recognition techniques.
FOCA: A Methodology for Ontology Evaluation

Modeling an ontology is a hard and time-consuming task. Although methodologies are useful for ontologists to create good ontologies, they do not help with the task of evaluating the quality of the ontology to be reused. For these reasons, it is imperative to evaluate the quality of the ontology after constructing it or before reusing it. Few studies usually present only a set of criteria and questions, but no guidelines to evaluate the ontology. The effort to evaluate an ontology is very high as there is a huge dependence on the evaluator’s expertise to understand the criteria and questions in depth. Moreover, the evaluation is still very subjective. This study presents a novel methodology for ontology evaluation, taking into account three fundamental principles: i) it is based on the Goal, Question, Metric approach for empirical evaluation; ii) the goals of the methodologies are based on the roles of knowledge representations combined with specific evaluation criteria; iii) each ontology is evaluated according to the type of ontology. The methodology was empirically evaluated using different ontologists and ontologies of the same domain. The main contributions of this study are: i) defining a step-by-step approach to evaluate the quality of an ontology; ii) proposing an evaluation based on the roles of knowledge representations; iii) the explicit difference of the evaluation according to the type of the ontology iii) a questionnaire to evaluate the ontologies; iv) a statistical model that automatically calculates the quality of the ontologies.
Comparison of applications for educational data mining in Engineering Education

Currently there are many techniques based on information technology and communication aimed at assessing the performance of students. Data mining applied in the educational field (educational data mining) is one of the most popular techniques that are used to provide feedback with regard to the teaching-learning process. In recent years there have been a large number of open source applications in the area of educational data mining. These tools have facilitated the implementation of complex algorithms for identifying hidden patterns of information in academic databases. The main objective of this paper is to compare the technical features of three open source tools (RapidMiner, Knime and Weka) as used in educational data mining. These features have been compared in a practical case study on the academic records of three engineering programs in an Ecuadorian university. This comparison has allowed us to determine which tool is most effective in terms of predicting student performance.
A low-profile dual polarized antenna with enhanced bandwidth for 2.4 GHz WLAN application

This paper proposes a novel low-profile dual polarized antenna for 2.4 GHz application. The proposed antenna consists of a circular patch with four curved T-stubs and a differential feeding network. Due to the parasitic loading of the curved T-stubs, the bandwidth has been improved. Good impedance matching and dual-polarization with low cross polarization have been achieved within 2.4–2.5 GHz, which is sufficient for WLAN application. The total thickness of the antenna is only 0.031A,o, which is low-profile when compared with its counterparts.
Design, Fabrication and Failure Analysis of Stretchable Electrical Routings

Stretchable microelectromechanical systems (MEMS) possess higher mechanical deformability and adaptability than devices based on conventional solid and flexible substrates, hence they are particularly desirable for biomedical, optoelectronic, textile and other innovative applications. The stretchability performance can be evaluated by the failure strain of the embedded routing and the strain applied to the elastomeric substrate. The routings are divided into five forms according to their geometry: straight; wavy; wrinkly; island-bridge; and conductive-elastomeric. These designs are reviewed and their resistance-to-failure performance is investigated. The failure modeling, numerical analysis, and fabrication of routings are presented. The current review concludes with the essential factors of the stretchable electrical routing for achieving high performance, including routing angle, width and thickness. The future challenges of device integration and reliability assessment of the stretchable routings are addressed.
Gray-level Image Enhancement By Particle Swarm Optimization

Particle Swarm Optimization (PSO) algorithms represent a new approach for optimization. In this paper image enhancement is considered as an optimization problem and PSO is used to solve it. Image enhancement is mainly done by maximizing the information content of the enhanced image with intensity transformation function. In the present work a parameterized transformation function is used, which uses local and global information of the image. Here an objective criterion for measuring image enhancement is used which considers entropy and edge information of the image. We tried to achieve the best enhanced image according to the objective criterion by optimizing the parameters used in the transformation function with the help of PSO. Results are compared with other enhancement techniques, viz. histogram equalization, contrast stretching and genetic algorithm based image enhancement.
Pink Breast Milk: Serratia marcescens Colonization

Breast milk can turn pink with Serratia marcescens colonization, this bacterium has been associated with several diseases and even death. It is seen most commonly in the intensive care settings. Discoloration of the breast milk can lead to premature termination of nursing. We describe two cases of pink-colored breast milk in which S. marsescens was isolated from both the expressed breast milk. Antimicrobial treatment was administered to the mothers. Return to breastfeeding was successful in both the cases. Pink breast milk is caused by S. marsescens colonization. In such cases,early recognition and treatment before the development of infection is recommended to return to breastfeeding.
The gas transmission problem solved by an extension of the Simplex algorithm

The problem of distributing gas through a network of pipelines is formulated as a cost minimization subject to nonlinear flow-pressure relations, material balances and pressure bounds. The solution method is based on piecewise linear approximations of the nonlinear flowpressure relations. The approximated problem is solved by an extension of the Simplex method. The solution method is tested on real world data and compared with alternative solution methods.
A Fast Enhanced Algorithm of PRI Transform

The problem of estimating pulse repetition interval (PRI) of an interleaved pulse train which consist of several independent radar signals, is the main issue of signal processing in electronic support systems. PRI Transform algorithm is one of the well known and effective methods of PRI detection which is capable of detecting several close jittered signals and surpassing subharmonics, but have some drawbacks especially because of small PRI dynamic range and heavy computations. In this paper a modified PRI transform is introduced which manage wide range of PRIs simultaneously, and speed up the algorithm by significantly reducing the computations. Moreover an efficient threshold is set for increasing detection rate. Simulation results proves that the modified algorithm shows much better performance in case of wide range PRI detection and processing speed while retaining the advantages the original algorithm.
Current concepts on the management of tooth wear: part 4. An overview of the restorative techniques and dental materials commonly applied for the management of tooth wear

This final article of the four part series on the current concepts of tooth wear will provide the reader with an evaluation of the data available in the contemporary literature with regards to the survival analysis of differing restorative materials, and their respective methods of application to treat tooth wear. It is vital that the dental operator is familiar with the role of differing materials which may be used to restore the worn dentition, some of which may prove to be more suitable for the management of particular patterns of tooth wear than others. The active management of tooth wear unfortunately commits the patient to a lifelong need for considerable maintenance, and it is imperative that this is understood from the outset.
An annotation management system for relational databases

We present an annotation management system for relational databases. In this system, every piece of data in a relation is assumed to have zero or more annotations associated with it and annotations are propagated along, from the source to the output, as data is being transformed through a query. Such an annotation management system could be used for understanding the provenance (aka lineage) of data, who has seen or edited a piece of data or the quality of data, which are useful functionalities for applications that deal with integration of scientific and biological data. We present an extension, pSQL, of a fragment of SQL that has three different types of annotation propagation schemes, each useful for different purposes. The default scheme propagates annotations according to where data is copied from. The default-all scheme propagates annotations according to where data is copied from among all equivalent formulations of a given query. The custom scheme allows a user to specify how annotations should propagate. We present a storage scheme for the annotations and describe algorithms for translating a pSQL query under each propagation scheme into one or more SQL queries that would correctly retrieve the relevant annotations according to the specified propagation scheme. For the default-all scheme, we also show how we generate finitely many queries that can simulate the annotation propagation behavior of the set of all equivalent queries, which is possibly infinite. The algorithms are implemented and the feasibility of the system is demonstrated by a set of experiments that we have conducted.
Alternative methods for the Plasmodium falciparum artemisinin ring-stage survival assay with increased simplicity and parasite stage-specificity

Artemisinin-based combination therapy is recommended to treat Plasmodium falciparum worldwide, but observations of longer artemisinin (ART) parasite clearance times (PCTs) in Southeast Asia are widely interpreted as a sign of potential ART resistance. In search of an in vitro correlate of in vivo PCT after ART treatment, a ring-stage survival assay (RSA) of 0–3 h parasites was developed and linked to polymorphisms in the Kelch propeller protein (K13). However, RSA remains a laborious process, involving heparin, Percoll gradient, and sorbitol treatments to obtain rings in the 0–3 h window. Here two alternative RSA protocols are presented and compared to the standard Percoll-based method, one highly stage-specific and one streamlined for laboratory application. For all protocols, P. falciparum cultures were synchronized with 5 % sorbitol treatment twice over two intra-erythrocytic cycles. For a filtration-based RSA, late-stage schizonts were passed through a 1.2 μm filter to isolate merozoites, which were incubated with uninfected erythrocytes for 45 min. The erythrocytes were then washed to remove lysis products and further incubated until 3 h post-filtration. Parasites were pulsed with either 0.1 % dimethyl sulfoxide (DMSO) or 700 nM dihydroartemisinin in 0.1 % DMSO for 6 h, washed twice in drug-free media, and incubated for 66–90 h, when survival was assessed by microscopy. For a sorbitol-only RSA, synchronized young (0–3 h) rings were treated with 5 % sorbitol once more prior to the assay and adjusted to 1 % parasitaemia. The drug pulse, incubation, and survival assessment were as described above. Ring-stage survival of P. falciparum parasites containing either the K13 C580 or C580Y polymorphism (associated with low and high RSA survival, respectively) were assessed by the described filtration and sorbitol-only methods and produced comparable results to the reported Percoll gradient RSA. Advantages of both new methods include: fewer reagents, decreased time investment, and fewer procedural steps, with enhanced stage-specificity conferred by the filtration method. Assessing P. falciparum ART sensitivity in vitro via RSA can be streamlined and accurately evaluated in the laboratory by filtration or sorbitol synchronization methods, thus increasing the accessibility of the assay to research groups.
Learning words and rules: abstract knowledge of word order in early sentence comprehension.

Children quickly acquire basic grammatical facts about their native language. Does this early syntactic knowledge involve knowledge of words or rules? According to lexical accounts of acquisition, abstract syntactic and semantic categories are not primitive to the language-acquisition system; thus, early language comprehension and production are based on verb-specific knowledge. The present experiments challenge this account: We probed the abstractness of young children's knowledge of syntax by testing whether 25- and 21-month-olds extend their knowledge of English word order to new verbs. In four experiments, children used word order appropriately to interpret transitive sentences containing novel verbs. These findings demonstrate that although toddlers have much to learn about their native languages, they represent language experience in terms of an abstract mental vocabulary. These abstract representations allow children to rapidly detect general patterns in their native language, and thus to learn rules as well as words from the start.
The Mathematical Theory of Information , and Applications ( Version 2 . 0 )

These lecture notes introduce some basic concepts from Shannon’s information theory, such as (conditional) Shannon entropy, mutual information, and Rényi entropy, as well as a number of basic results involving these notions. Subsequently, well-known bounds on perfectly secure encryption, source coding (i.e. data compression), and reliable communication over unreliable channels are discussed. We also cover and prove the elegant privacy amplification theorem. This provides a means to mod out the adversary’s partial information and to distill a highly secret key. It is a key result in theoretical cryptography, and a primary starting point for the very active subarea of unconditional security.