nils-herrmann/clean_scirepeval huggingface dataset:
Exhumation of high-pressure metamorphic rocks in a subduction channel : A numerical simulation

High-pressure metamorphic rocks provide evidence that in subduction zones material can return from depths of more than 100 km to the surface. The pressure-temperature paths recorded by these rocks are variable, mostly revealing cooling during decompression, while the time constraints are generally narrow and indicate that the exhumation rates can be on the order of plate velocities. As such, subduction cannot be considered as a single pass process; instead, return flow of a considerable portion of crustal and upper mantle material must be accounted for. Our numerical simulations provide insight into the self-organizing large-scale flow patterns and temperature field of subduction zones, primarily controlled by rheology, phase transformations, fluid budget, and heat transfer, which are all interrelated. They show the development of a subduction channel with forced return flow of low-viscosity material and progressive widening by hydration of the mantle wedge. The large-scale structures and the array of pressure-temperature paths obtained by these simulations favorably compare to the record of natural rocks and the structure of high-pressure metamorphic areas.
2 Experiment 1 : Traditional NEAT Control 2 . 1 Motivation and Procedure

Walking is a classic task for both evolving and engineered robots. The task is complex yet easy to score and holds much biological and practical relevance. Here, we look specifically at evolving voxel-based soft robots and how current evolutionary algorithms can be improved to better emulate biological systems and develop more sophisticated locomotor behavior. We assess and attempt to remedy the shortcomings of previous studies on evolving soft robots using compositional pattern-producing networks (CPPNs). We found that CPPNs are not necessary for the emergence of bilateral symmetry, although the relatively more complex CPPN networks performed better than traditional NEAT. We also propose and outline implementation of a biologically-inspired soft robot using a system of coevolving central pattern generators (CPGs).
A Twitter Corpus for Hindi-English Code Mixed POS Tagging

Code-mixing is a linguistic phenomenon where multiple languages are used in the same occurrence that is increasingly common in multilingual societies. Codemixed content on social media is also on the rise, prompting the need for tools to automatically understand such content. Automatic Parts-of-Speech (POS) tagging is an essential step in any Natural Language Processing (NLP) pipeline, but there is a lack of annotated data to train such models. In this work, we present a unique language tagged and POS-tagged dataset of code-mixed English-Hindi tweets related to five incidents in India that led to a lot of Twitter activity. Our dataset is unique in two dimensions: (i) it is larger than previous annotated datasets and (ii) it closely resembles typical real-world tweets. Additionally, we present a POS tagging model that is trained on this dataset to provide an example of how this dataset can be used. The model also shows the efficacy of our dataset in enabling the creation of codemixed social media POS taggers.
Does In Utero Exposure to Heavy Maternal Smoking Induce Nicotine Withdrawal Symptoms in Neonates?

Maternal drug use during pregnancy is associated with fetal passive addiction and neonatal withdrawal syndrome. Cigarette smoking—highly prevalent during pregnancy—is associated with addiction and withdrawal syndrome in adults. We conducted a prospective, two-group parallel study on 17 consecutive newborns of heavy-smoking mothers and 16 newborns of nonsmoking, unexposed mothers (controls). Neurologic examinations were repeated at days 1, 2, and 5. Finnegan withdrawal score was assessed every 3 h during their first 4 d. Newborns of smoking mothers had significant levels of cotinine in the cord blood (85.8 ± 3.4 ng/mL), whereas none of the controls had detectable levels. Similar findings were observed with urinary cotinine concentrations in the newborns (483.1 ± 2.5 μg/g creatinine versus 43.6 ± 1.5 μg/g creatinine; p = 0.0001). Neurologic scores were significantly lower in newborns of smokers than in control infants at days 1 (22.3 ± 2.3 versus 26.5 ± 1.1; p = 0.0001), 2 (22.4 ± 3.3 versus 26.3 ± 1.6; p = 0.0002), and 5 (24.3 ± 2.1 versus 26.5 ± 1.5; p = 0.002). Neurologic scores improved significantly from day 1 to 5 in newborns of smokers (p = 0.05), reaching values closer to control infants. Withdrawal scores were higher in newborns of smokers than in control infants at days 1 (4.5 ± 1.1 versus 3.2 ± 1.4; p = 0.05), 2 (4.7 ± 1.7 versus 3.1 ± 1.1; p = 0.002), and 4 (4.7 ± 2.1 versus 2.9 ± 1.4; p = 0.007). Significant correlations were observed between markers of nicotine exposure and neurologic-and withdrawal scores. We conclude that withdrawal symptoms occur in newborns exposed to heavy maternal smoking during pregnancy.
Fast Joint Compression and Summarization via Graph Cuts

Extractive summarization typically uses sentences as summarization units. In contrast, joint compression and summarization can use smaller units such as words and phrases, resulting in summaries containing more information. The goal of compressive summarization is to find a subset of words that maximize the total score of concepts and cutting dependency arcs under the grammar constraints and summary length constraint. We propose an efficient decoding algorithm for fast compressive summarization using graph cuts. Our approach first relaxes the length constraint using Lagrangian relaxation. Then we propose to bound the relaxed objective function by the supermodular binary quadratic programming problem, which can be solved efficiently using graph max-flow/min-cut. Since finding the tightest lower bound suffers from local optimality, we use convex relaxation for initialization. Experimental results on TAC2008 dataset demonstrate our method achieves competitive ROUGE score and has good readability, while is much faster than the integer linear programming (ILP) method.
Up-Regulation of Neurotrophic Factors by Cinnamon and its Metabolite Sodium Benzoate: Therapeutic Implications for Neurodegenerative Disorders

This study underlines the importance of cinnamon, a widely-used food spice and flavoring material, and its metabolite sodium benzoate (NaB), a widely-used food preservative and a FDA-approved drug against urea cycle disorders in humans, in increasing the levels of neurotrophic factors [e.g., brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NT-3)] in the CNS. NaB, but not sodium formate (NaFO), dose-dependently induced the expression of BDNF and NT-3 in primary human neurons and astrocytes. Interestingly, oral administration of ground cinnamon increased the level of NaB in serum and brain and upregulated the levels of these neurotrophic factors in vivo in mouse CNS. Accordingly, oral feeding of NaB, but not NaFO, also increased the level of these neurotrophic factors in vivo in the CNS of mice. NaB induced the activation of protein kinase A (PKA), but not protein kinase C (PKC), and H-89, an inhibitor of PKA, abrogated NaB-induced increase in neurotrophic factors. Furthermore, activation of cAMP response element binding (CREB) protein, but not NF-κB, by NaB, abrogation of NaB-induced expression of neurotrophic factors by siRNA knockdown of CREB and the recruitment of CREB and CREB-binding protein to the BDNF promoter by NaB suggest that NaB exerts its neurotrophic effect through the activation of CREB. Accordingly, cinnamon feeding also increased the activity of PKA and the level of phospho-CREB in vivo in the CNS. These results highlight a novel neutrophic property of cinnamon and its metabolite NaB via PKA – CREB pathway, which may be of benefit for various neurodegenerative disorders.
Transient Nanoscopic Phase Separation in Biological Lipid Membranes Resolved by Planar Plasmonic Antennas.

Nanoscale membrane assemblies of sphingolipids, cholesterol, and certain proteins, also known as lipid rafts, play a crucial role in facilitating a broad range of important cell functions. Whereas on living cell membranes lipid rafts have been postulated to have nanoscopic dimensions and to be highly transient, the existence of a similar type of dynamic nanodomains in multicomponent lipid bilayers has been questioned. Here, we perform fluorescence correlation spectroscopy on planar plasmonic antenna arrays with different nanogap sizes to assess the dynamic nanoscale organization of mimetic biological membranes. Our approach takes advantage of the highly enhanced and confined excitation light provided by the nanoantennas together with their outstanding planarity to investigate membrane regions as small as 10 nm in size with microsecond time resolution. Our diffusion data are consistent with the coexistence of transient nanoscopic domains in both the liquid-ordered and the liquid-disordered microscopic phases of multicomponent lipid bilayers. These nanodomains have characteristic residence times between 30 and 150 μs and sizes around 10 nm, as inferred from the diffusion data. Thus, although microscale phase separation occurs on mimetic membranes, nanoscopic domains also coexist, suggesting that these transient assemblies might be similar to those occurring in living cells, which in the absence of raft-stabilizing proteins are poised to be short-lived. Importantly, our work underscores the high potential of photonic nanoantennas to interrogate the nanoscale heterogeneity of native biological membranes with ultrahigh spatiotemporal resolution.
A Frequency-Reconfigurable Dual-Band Low-Profile Monopolar Antenna

A concept of a low-profile monopolar antenna operating in two independently reconfigurable frequency bands is introduced in this paper. For the lower band, the design utilizes a center-fed patch with shorting rods at its edges, forcing the patch to radiate as an equivalent magnetic-current loop. For the upper band, another magnetic current loop is created by adding four symmetrical resonant slots on the patch, which radiates almost independently of the shorted patch. To allow coverage of a larger bandwidth, two sets of varactor diodes are used to independently control the resonance frequencies of the two bands. As a validation of the proposed concept, two antenna prototypes, with and without reconfigurabilities, have been optimized and fabricated. Measurement results show that the low-profile reconfigurable monopolar antenna achieves two independent tunable bands, with −10-dB-tuning ranges of 31% and 22% centered at about 0.9 and 1.7 GHz, respectively. The antenna height is only <inline-formula> <tex-math notation="LaTeX">$0.020\lambda _{\text {min}}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\lambda _{\text {min}}$ </tex-math></inline-formula> is the free-space wavelength at the minimum operating frequency. Importantly, stable omnidirectional patterns and vertical polarization are consistently achieved across both tuning ranges.
Neighbor-Aware Search for Approximate Labeled Graph Matching using the Chi-Square Statistics

Labeled graphs provide a natural way of representing entities, relationships and structures within real datasets such as knowledge graphs and protein interactions. Applications such as question answering, semantic search, and motif discovery entail efficient approaches for subgraph matching involving both label and structural similarities. Given the NP-completeness of subgraph isomorphism and the presence of noise, approximate graph matching techniques are required to handle queries in a robust and real-time manner. This paper presents a novel technique to characterize the subgraph similarity based on statistical significance captured by chi-square statistic. The statistical significance model takes into account the background structure and label distribution in the neighborhood of vertices to obtain the best matching subgraph and, therefore, robustly handles partial label and structural mismatches. Based on the model, we propose two algorithms, VELSET and NAGA, that, given a query graph, return the top-k most similar subgraphs from a (large) database graph. While VELSET is more accurate and robust to noise, NAGA is faster and more applicable for scenarios with low label noise. Experiments on large real-life graph datasets depict significant improvements in terms of accuracy and running time in comparison to the state-of-the-art methods.
A Taxonomy of Evaluation Approaches in Software Engineering

As in any academic discipline, the evaluation of proposed methodologies and techniques is of vital importance for assessing the validity of novel ideas or findings in Software Engineering. Over the years, a large number of evaluation approaches have been employed, some of them drawn from other domains and other particularly developed for the needs of software engineering related research. In this paper we present the results of a survey of evaluation techniques that have been utilized in research papers that appeared in three leading software engineering journal and propose a taxonomy of evaluation approaches which might be helpful towards the organization of knowledge regarding the different strategies for the validation of research outcomes. The applicability of the proposed taxonomy has been evaluated by classifying the articles retrieved from ICSE'2012.
Analysis and Visualization of Sentiment and Emotion on Crisis Tweets

Understanding how people communicate during disasters is important for creating systems to support this communication. Twitter is commonly used to broadcast information and to organize support during times of need. During the 2010 Gulf Oil Spill, Twitter was utilized for spreading information, sharing firsthand observations, and to voice concern about the situation. Through building a series of classifiers to detect emotion and sentiment, the distribution of emotion during the Gulf Oil Spill can be analyzed and its propagation compared against released information and corresponding events. We contribute a series of emotion classifiers and a prototype collaborative visualization of the results and discuss their implications.
Techno-economic modelling and cost functions of CO2 capture processes

The paper contributes to techno-economic modelling of CO2 capture process in coalfired power plants. Technological options of CO2 capture have been chosen and cost estimation relationships (CERs) for the chosen options provided. The CERs are being fitted into a developed overall model of carbon capture and sequestration. Functions relating capital costs, operation and maintenance costs and total annualised costs to plant electricity output and amount of CO2 avoided have been developed. The influence of interest rates and plant life has also been analysed.
A 100–300-GHz Free-Space Scalar Network Analyzer Using Compact Tx and Rx Modules

This paper presents a 100-300-GHz quasi-optical network analyzer using compact transmitter and receiver modules. The transmitter includes a wideband double bow-tie slot antenna and employs a Schottky diode as a frequency harmonic multiplier. The receiver includes a similar antenna, a Schottky diode used as a subharmonic mixer, and an LO/IF diplexer. The 100-300-GHz RF signals are the 5th-11th harmonics generated by the frequency multiplier when an 18-27-GHz LO signal is applied. The measured transmitter conversion gain with Pin = 18$ dBm is from -35 to -59 dB for the 5th-11th harmonic, respectively, and results in a transmitter EIRP from +3 to -20 dBm up to 300 GHz. The measured mixer conversion gain is from -30 to -47 dB at the 5th-11th harmonic, respectively. The system has a dynamic range > 60 dB at 200 GHz in a 100-Hz bandwidth for a transmit and receive system based on 12-mm lenses and spaced 60 cm from each other. Frequency-selective surfaces at 150 and 200 GHz are tested by the proposed design and their measured results agree with simulations. Application areas are low-cost scalar network analyzers for wideband quasi-optical 100 GHz-1 THz measurements.
Evidence for additional FREM1 heterogeneity in Manitoba oculotrichoanal syndrome

Manitoba Oculotrichoanal (MOTA) syndrome is an autosomal recessive disorder present in First Nations families that is characterized by ocular (cryptophthalmos), facial, and genital anomalies. At the commencement of this study, its genetic basis was undefined.



Homozygosity analysis was employed to map the causative locus using DNA samples from four probands of Cree ancestry. After single nucleotide polymorphism (SNP) genotyping, data were analyzed and exported to PLINK to identify regions identical by descent (IBD) and common to the probands. Candidate genes within and adjacent to the IBD interval were sequenced to identify pathogenic variants, with analyses of potential deletions or duplications undertaken using the B-allele frequency and log(2) ratio of SNP signal intensity.



Although no shared IBD region >1 Mb was evident on preliminary analysis, adjusting the criteria to permit the detection of smaller homozygous IBD regions revealed one 330 Kb segment on chromosome 9p22.3 present in all 4 probands. This interval comprising 152 SNPs, lies 16 Kb downstream of FRAS1-related extracellular matrix protein 1 (FREM1), and no copy number variations were detected either in the IBD region or FREM1. Subsequent sequencing of both genes in the IBD region, followed by FREM1, did not reveal any mutations.



This study illustrates the utility of studying geographically isolated populations to identify genomic regions responsible for disease through analysis of small numbers of affected individuals. The location of the IBD region 16 kb from FREM1 suggests the phenotype in these patients is attributable to a variant outside of FREM1, potentially in a regulatory element, whose identification may prove tractable to next generation sequencing. In the context of recent identification of FREM1 coding mutations in a proportion of MOTA cases, characterization of such additional variants offers scope both to enhance understanding of FREM1's role in cranio-facial biology and may facilitate genetic counselling in populations with high prevalences of MOTA to reduce the incidence of this disorder.
Maxillary labial frenum attachment in children.

The distribution of the attachment of the maxillary labial frenum in the children of different ethnic backgrounds has not been studied extensively.



The purpose of this cross-sectional study was to examine the prevalence of the various types of maxillary labial frenum attachment in the children of different ethnic backgrounds.



Children (aged 1-18) attending a public health clinic in Lavrion, Greece, were clinically examined for maxillary frenum attachment location. Demographic information was recorded. Parents provided written informed consent.



The examined children were 226, with mean (± standard deviation) age of 8.5 ± 3.0 years. They were of Greek (51%), Albanian (20%), Turkish (12%), and Afghan (11%) descent. The prevalence of the maxillary labial frenum attachment was mucosal (10.2%), gingival (41.6%), papillary (22.1%), and papillary penetrating (26.1%). Frenum attachment differed significantly by age (P = 0.001). The age of children with mucosal- or gingival-type frenum was significantly greater than the age of children with papillary penetrating-type frenum. Frenum attachment did not differ by gender or ethnic background (P ≥ 0.20).



The results of this study suggest that, in children, ethnic background and gender are not associated with maxillary labial frenum attachment type, whereas age is strongly associated.
A Novel Method for Disease Recognition and Cure Time Prediction Based on Symptoms

Healthcare is a sector where decisions usually have very high-risk and high-cost associated with them. One bad choice can cost a person's life. With diseases like Swine Flu on the rise, which have symptoms quite similar to common cold, it's very difficult for people to differentiate between medical conditions. We propose a novel method for recognition of diseases and prediction of their cure time based on the symptoms. We do this by assigning different coefficients to each symptom of a disease, and filtering the dataset with the severity score assigned to each symptom by the user. The diseases are identified based on a numerical value calculated in the fashion mentioned above. For predicting the cure time of a disease, we use reinforcement learning. Our algorithm takes into account the similarity between the condition of the current user and other users who have suffered from the same disease, and uses the similarity scores as weights in prediction of cure time. We also predict the current medical condition of user relative to people who have suffered from same disease.
Modeling of the HP-1430A feedthrough wide-band (28-ps) sampling head

The theory and construction of the HP-1430A feed-through sampling head are reviewed, and a model for the sampling head is developed from dimensional and electrical measurements in conjunction with electromagnetic, electronic, and network theory. The model was used to predict the sampling-head step response needed for the deconvolution of true input waveforms. The dependence of the sampling-head step response on the sampling diode bias is investigated. Calculations based on the model predict step response transition durations of 27.5 to 30.5 ps for diode reverse bias values of -1.76 to -1.63 V.
A Survey on Activities of Daily Living and Occupations of Upper Extremity Amputees

To assess prosthetic use by upper extremity amputees, and their difficulties with prostheses in activities of daily living and occupations.



This study is based on a survey of 307 subjects, who were using prostheses manufactured in the Center of Prosthetics and Orthotics. The survey questionnaire included items about general demographic characteristics, side and level of amputation, type of prosthesis and its use, and difficulties in the activities of daily living, employment and driving.



The most common type of prosthesis was the cosmetic hand type (80.2%). There were no statistically significant correlations between satisfaction with prosthesis and the amputation level or type of prosthesis. The most common difficulties in daily living activities experienced by amputees were lacing shoes, removing bottle-tops with a bottle opener, and using scissors. Only 7.3% of amputees received rehabilitation services. Less than half of the amputees (44.7%) used their prostheses for eight or more hours a day, and 76.9% used their prostheses for regular or irregular cosmetic purposes. After amputation, most of the respondents (69.0%) became unemployed or changed workplaces.



In our study, respondents preferred cosmetic usage to functional usage. Only 30.0% of respondents reported satisfaction with their prostheses. Many of the amputees had difficulties in complex tasks and either changed jobs or became unemployed. Clerical workers were the occupation group, which was most likely to return to work. The development of a more functional prosthetic hand and additional rehabilitation services are required.
A conceptual model of e-learning based on cloud computing adoption in higher education institutions

Cloud computing refers to adopting advanced virtualized resources for high scalability that can be shared with end users. Utilization of this technology is expediting in the world to intensify the potential of cloud computing based on E-learning in higher education institutions. Due to various apparent reasons, some higher education institutions are disinclined to relocate internal services to cloud services. To develop a model for utilizing E-learning based on cloud computing is the focal point of this paper, which is based on two prominent theories: The Fit-Viability Model and Diffusion of Innovation, in addition to information culture factors. The main purpose of this model is to investigate the significant factors to exploit the cloud in the enhancement of E-learning in higher education institutions.