nils-herrmann/clean_scirepeval huggingface dataset:
CONSUMO ALIMENTAR E ESTADO NUTRICIONAL DE ADOLESCENTES ATIVOS E NAO ATIVOS FISICAMENTE

O risco aumentado para o desenvolvimento de Doencas Cronicas Nao Transmissiveis (DCNT) em decorrencia do consumo alimentar inadequado e o sedentarismo verificados em adolescentes, tem repercutido na saude desses individuos. Objetivou se comparar o estado nutricional e o consumo alimentar de adolescentes ativos com adolescentes nao ativos fisicamente, matriculados em um colegio particular do municipio de Limoeiro do Norte, Ceara. A amostra correspondeu a 72 alunos do 9deg ano do ensino fundamental da referida instituicao, os quais foram classificados em dois grupos, grupo dos estudantes ativos e grupo dos nao ativos. Foi aplicado um formulario contendo dados relativos a atividade fisica, pessoais, socioeconomicos, antropometricos e alimentares. No grupo de estudantes ativos, verificou se um numero maior (66,7% de individuos eutroficos. Quanto ao percentual de gordura corporal, observou se que a maioria (58,3% dos adolescentes ativos apresentou se dentro do padrao de normalidade. Verificou se no grupo ativo maior numero (91,7% de estudantes que nao apresentaram risco cardiovascular quando comparados aos nao ativos. O consumo alimentar encontrou se inadequado em ambos os grupos de adolescentes estudados. Concluiu se que o excesso de peso juntamente com o consumo alimentar inadequado, verificado entre os adolescentes nao ativos, sugere um risco aumentado para o desenvolvimento de DCNT nesse publico.  Food Consumption and Nutritional Status of Assets and Teens Not Physically ActiveThe increased risk for the development of Chronic Non communicable Diseases (NCDs) as a result of inadequate dietary intake and physical inactivity observed in adolescents has impacted the health of these individuals. The objective was to compare the nutritional status and food intake in adolescents with active physically active adolescents, enrolled in a private school in the municipality from Limoeiro do Norte, Ceara. The sample consisted of 72 students in the 9th grade of elementary school of that institution, which were classified into two groups, group of students and active group of non active. A form containing data regarding the physical activity, personal, socioeconomic, anthropometric and dietary was applied. In the group of active students there was an excess (66.7% of normal individuals. As for the percentage of body fat, it was observed that the majority (58.3% of active adolescents was within normal limits. It was found in the active group larger number (91.7% of students who did not have cardiovascular risk compared to non active. Dietary intake found to be inadequate in both groups of adolescents studied. It is concluded that excess weight along with adequate food intake observed among non active adolescents, suggests an increased risk for the development of NCDs in public.
Dynamics of community participation, student achievement and school management: the case of primary schools in a rural area of Malawi

School management in many sub Saharan African countries has been enhanced through community participation in an attempt to improve education quality. This study uses field research in a rural district of Malawi to assess how community and parent participation differs between schools, the intentions of communities and parents when carrying out activities in schools, and the mechanism promoting active participation in schools. In high achieving schools, but not in low achieving schools, communities and parents were actively involved in events aimed at improving student achievement. Communities and parents considered most highly prioritised activities that directly influenced student achievement, including hiring volunteer teachers, arranging extra classes and holding mock examinations. Community participation did not directly improve student achievement. Instead, when communities and parents actively participated in a school, school management improved, ensuring better student achievement. The implications of this finding are discussed in relation to community participation.
Digital Transformation in School Management and Culture

Quality becomes milestone for enhancing learning and teaching practices through contemporary standards. In this respect, considering the outcomes of transformation is essential. School management is an umbrella of facilitating learning and teaching. Therefore, role of digital transformation in school management is an intensified need to be underlined. In establishing school culture, school management plays an essential role that digital transformation effects the school management and culture for the quality in education. The nature of this research is qualitative. Reflective reports of headmasters are gathered as data to understand the readability and awareness of school management to the digital transformation in the case of North Cyprus. In addition, content analysis is done to realize the upcoming issues in school management and digital transformation. As this focus is demanding for developing countries especially North Cyprus, this research revealed that headmasters have awareness on the use of digital and techno logical facilities. They are aware on digital transformation although budget and application restrictions are revealed to apply this transformation.
SoundNet: Learning Sound Representations from Unlabeled Video
Venue: NIPS

We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild. We leverage the natural synchronization between vision and sound to learn an acoustic representation using two million unlabeled videos. Unlabeled video has the advantage that it can be economically acquired at massive scales, yet contains useful signals about natural sound. We propose a student teacher training procedure which transfers discriminative visual knowledge from well established visual recognition models into the sound modality using unlabeled video as a bridge. Our sound representation yields significant performance improvements over the state of the art results on standard benchmarks for acoustic scene/object classification. Visualizations suggest some high level semantics automatically emerge in the sound network, even though it is trained without ground truth labels.
Anticipating Visual Representations from Unlabeled Video
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently learning this knowledge is through readily available unlabeled video. We present a framework that capitalizes on temporal structure in unlabeled video to learn to anticipate human actions and objects. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. Visual representations are a promising prediction target because they encode images at a higher semantic level than pixels yet are automatic to compute. We then apply recognition algorithms on our predicted representation to anticipate objects and actions. We experimentally validate this idea on two datasets, anticipating actions one second in the future and objects five seconds in the future.
Instant Integrated Ultradeep Quantitative structural Membrane Proteomics Discovered Post translational Modification Signatures for Human Cys loop Receptor Subunit Bias*
Venue: Molecular Cellular Proteomics

Neurotransmitter ligand gated ion channels (LGICs) are widespread and pivotal in brain functions. Unveiling their structure function mechanisms is crucial to drive drug discovery, and demands robust proteomic quantitation of expression, post translational modifications (PTMs) and dynamic structures. Yet unbiased digestion of these modified transmembrane proteins at high efficiency and peptide reproducibility poses the obstacle. Targeting both enzyme substrate contacts and PTMs for peptide formation and detection, we devised flow and detergent facilitated protease and de PTM digestions for deep sequencing (FDD) method that combined omni compatible detergent, tandem immobilized protease/PNGase columns, and Cys selective reduction/alkylation, to achieve streamlined ultradeep peptide preparation within minutes not days, at high peptide reproducibility and low abundance bias. FDD transformed enzyme protein contacts into equal catalytic travel paths through enzyme excessive columns regardless of protein abundance, removed products instantly preventing inhibition, tackled intricate structures via sequential multiple micro digestions along the flow, and precisely controlled peptide formation by flow rate. Peptide stage reactions reduced steric bias; low contamination deepened MS/MS scan; distinguishing disulfide from M oxidation and avoiding gain/loss artifacts unmasked protein endogenous oxidation states. Using a recent interactome of 285 kDa human GABA type A receptor, this pilot study validated FDD platform's applicability to deep sequencing (up to 99% coverage) H/D exchange and TMT based structural mapping. FDD discovered novel subunit specific PTM signatures, including unusual nontop surface N glycosylations, that may drive subunit biases in human Cys loop LGIC assembly and pharmacology, by redefining subunit/ligand interfaces and connecting function domains.
A survey of the all pairs shortest paths problem and its variants in graphs

There has been a great deal of interest in the computation of distances and shortest paths problem in graphs which is one of the central, and most studied, problems in (algorithmic) graph theory. In this paper, we survey the exact results of the static version of the all pairs shortest paths problem and its variants namely, the Wiener index, the average distance, and the minimum average distance spanning tree (MAD tree in short) in graphs (focusing mainly on algorithmic results for such problems) Along the way we also mention some important open issues and further research directions in these areas.
Robust Scene Text Recognition with Automatic Rectification
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Recognizing text in natural images is a challenging task with many unsolved problems. Different from those in documents, words in natural images often possess irregular shapes, which are caused by perspective distortion, curved character placement, etc. We propose RARE (Robust text recognizer with Automatic REctification) a recognition model that is robust to irregular text. RARE is a speciallydesigned deep neural network, which consists of a Spatial Transformer Network (STN) and a Sequence Recognition Network (SRN) In testing, an image is firstly rectified via a predicted Thin Plate Spline (TPS) transformation, into a more "readable" image for the following SRN, which recognizes text through a sequence recognition approach. We show that the model is able to recognize several types of irregular text, including perspective text and curved text. RARE is end to end trainable, requiring only images and associated text labels, making it convenient to train and deploy the model in practical systems. State of the art or highly competitive performance achieved on several benchmarks well demonstrates the effectiveness of the proposed model.
Strokelets: A Learned Multi Scale Mid Level Representation for Scene Text Recognition
Venue: IEEE Transactions on Image Processing

In this paper, we are concerned with the problem of automatic scene text recognition, which involves localizing and reading characters in natural images. We investigate this problem from the perspective of representation and propose a novel multi scale representation, which leads to accurate, robust character identification and recognition. This representation consists of a set of mid level primitives, termed strokelets, which capture the underlying substructures of characters at different granularities. The Strokelets possess four distinctive advantages: 1) usability: automatically learned from character level annotations; 2) robustness: insensitive to interference factors; 3) generality: applicable to variant languages; and 4) expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of the strokelets and demonstrate the effectiveness of the text recognition algorithm built upon the strokelets. Moreover, we show the method to incorporate the strokelets to improve the performance of scene text detection.
Improving Bank's Customer Service on the Basis of Quality Management Tools

In the current market conditions, when the difference between the characteristics of services is becoming less noticeable, for saving their leading positions and improving competitiveness the loan companies should pay more attention to the relationship with different customer groups. The problem of obtaining service requirements from the customers, the necessary information about the quality of work with services and bank departments, the need for conducting the surveys and studies, analysis of comments and suggestions are especially relevant for the Russian regional banks. This article focuses on the application of quality management tools for improving the process of bank's customer service. The article presents results of the study conducted by the authors in 2011 and 2015 and aimed to confirm the hypothesis that the use of the tools of quality management in banking activities of customer service improves the competitiveness of the bank, due to better satisfaction of customers' requirements. The application of QFD and SERVQUAL methodologies is emphasized, the methods of questionnaire and expert survey were used in the process of the study, the satisfaction of different customers' categories was assessed. The object of the study was regional banks of Russia, located in the Republic of Mordovia. The study resulted in practical recommendations for improving the process of customer service in the regional Russian banks, as well as the proposal of the scheme of process management for customer service at the bank on the basis of various quality management tools.
Improving a Credit Scoring Model by Incorporating Bank Statement Derived Features
Venue: ArXiv

In this paper, we investigate the extent to which features derived from bank statements provided by loan applicants, and which are not declared on an application form, can enhance a credit scoring model for a New Zealand lending company. Exploring the potential of such information to improve credit scoring models in this manner has not been studied previously. We construct a baseline model based solely on the existing scoring features obtained from the loan application form, and a second baseline model based solely on the new bank statement derived features. A combined feature model is then created by augmenting the application form features with the new bank statement derived features. Our experimental results using ROC analysis show that a combined feature model performs better than both of the two baseline models, and show that a number of the bank statement derived features have value in improving the credit scoring model. The target data set used for modelling was highly imbalanced, and Naive Bayes was found to be the best performing model, and outperformed a number of other classifiers commonly used in credit scoring, suggesting its potential for future use on highly imbalanced data sets.
Predicting the Dynamic Response of Friction Dissipative Foundations Using a Modified Newmark Model

This paper analyses the dynamic behaviour of a seismic isolation technology adopted for the foundations of the towers of two recent long span bridges, consisting of a dissipative layer of granular soil placed at the contact of the foundation caisson with a group of foundation piles. The fundamental aspects of this isolation device is studied through a three dimensional modified Newmark model that accounts for the multi directionality of the seismic motion and includes a simplified description of the dynamic response of the superstructure. This simplified model is expressed in a rigorous non dimensional formulation and is validated against the results of three dimensional dynamic analysis of the soil structure interaction for a specific case of a suspension bridge. Taking advantage of the non dimensional formulation of the simplified dynamic model, a parametric study of general validity is carried out, that highlights the most significant factors influencing the performance of the dissipative device.
Dermatomyositis as a symptom of primary lung cancer: A case report and review of the literature.
Venue: Oncology letters

Dermatomyositis (DM) is a rare autoimmune disease associated with manifestations of proximal muscle weakness and typical cutaneous skin rash. Patients with DM have a higher incidence rate of cancer, compared with the general population, since DM usually presents as a paraneoplastic syndrome. The present study reports a case of a 69 year old male patient presenting with typical cutaneous rash and aggravating proximal muscle weakness for 3 months. When first admitted to the Department of Dermatology of Shandong Provincial Hospital Affiliated to Shandong University (Jinan, China) the patient was diagnosed with DM. Subsequently, chest computed tomography scan revealed a neoplasm located in the right upper lobe. Video assisted thoracoscopic surgery right upper lobectomy and lymph node dissection were then performed. The patient experienced a fast recovery from the surgery. The removal of the neoplasm clearly improved the symptoms of DM. Considering that DM is usually associated with the existence of a tumor, cases of DM require a thorough diagnostic investigation in order to exclude an underlying malignancy.
Correlation between lung neoplasm and serum level of osteopontin: A meta analysis
Venue: Biomedical reports

The aim of this meta analysis was to evaluate the clinical significance of serum osteopontin (OPN) levels in lung neoplasm in patients to establish a novel diagnostic score model. Articles were identified by searching the PubMed, Web of Science, Google Scholar, China National Knowledge Infrastructure, and Wang Fang databases. Studies identified were pooled, and the standardized mean difference (SMD) and its corresponding 95% confidence interval (CI) were calculated. Subgroup analyses and publication bias detection were also conducted. Version 12.0 STATA software was used for statistical analysis. A final analysis of 1,327 subjects together (740 patients with lung neoplasms and 587 controls) was performed from 10 clinical case control studies. The meta analysis results showed a positive association between serum OPN levels and lung neoplasm (SMD=5.59, 95% CI: 1.85 3.32, P<0.001) The subgroup analysis by ethnicity detected that high levels of serum OPN may be the main risk factor for lung neoplasms in Asians (SMD=1.76, 95% CI: 1.24 2.29, P<0.001) but not in Caucasians (P=0.072) In conclusion, the present meta analysis indicated that serum OPN levels were generally elevated in lung neoplasm patients, and thus, serum levels of OPN may be useful in diagnosing lung neoplasm in certain population groups.
Rating friends: the effect of personal connections on credit ratings

Using a large sample of US public debt issues we show that personal connections between directors of issuing companies and rating agencies result in higher credit ratings. We estimate the average effect to be about one notch. Moreover, our tests indicate that issues by connected firms are 30% more likely to be rated A3. Results are robust to several alternative tests including additional controls for managerial traits, firm fixed effects, and propensity score matching. Furthermore, our tests on default rates and bond yields suggest that personal connections act as a mechanism to reduce asymmetric information between the rating agency and the issuer.
A STUDY ON CUSTOMER PERCEPTION OF ORGANIC AND INORGANIC PRODUCTS IN CITY OF CHENNAI

As the organic market continues to grow, interest in Chennai consumers purchasing organic food and consumption behavior have emerged as essential research topics. The aim of the present study was to obtain an in depth understanding of Chennai consumers purchasing and consumption behavior with regard to organic food products. Consumer behavior models were employed for comparison which best serve for the purpose: Rational Based Theory and Value based Theory. Based on the behavior and moral models constructs, different Men, Women's organic food consumer groups were profiled. In addition, with the increasing consumer demand for health, food safety, and nutrition, the relationship between individual's health consciousness and their organic food purchasing and consumption behavior was also examined.The participants in this study were a self selected sample of Men, Women living in Chennai. The study also determined that based on the constructs of the theoretical framework, individual's self perceived subjective knowledgehad a significant direct influence on consumer organic food purchasing and consumption behavior. Respondent's health consciousness was found to be significantly correlated with their organic food purchasing and consumption behavior.It is suggested that marketers of organic food products allocate their major efforts and resources in providing more organic food related information when targeting consumers, rather than seeking endorsements from celebrities, the media, or related advocate groups.
Multi shape active composites by 3D printing of digital shape memory polymers
Venue: Scientific reports

Recent research using 3D printing to create active structures has added an exciting new dimension to 3D printing technology. After being printed, these active, often composite, materials can change their shape over time; this has been termed as 4D printing. In this paper, we demonstrate the design and manufacture of active composites that can take multiple shapes, depending on the environmental temperature. This is achieved by 3D printing layered composite structures with multiple families of shape memory polymer (SMP) fibers digital SMPs with different glass transition temperatures (Tg) to control the transformation of the structure. After a simple single step thermomechanical programming process, the fiber families can be sequentially activated to bend when the temperature is increased. By tuning the volume fraction of the fibers, bending deformation can be controlled. We develop a theoretical model to predict the deformation behavior for better understanding the phenomena and aiding the design. We also design and print several flat 2D structures that can be programmed to fold and open themselves when subjected to heat. With the advantages of an easy fabrication process and the controllable multi shape memory effect, the printed SMP composites have a great potential in 4D printing applications.
In situ observation of strain and phase transformation in plastically deformed 301 austenitic stainless steel

To inform the design of superior transformation induced plasticity (TRIP) steels, it is important to understand what happens at the microstructural length scales. In this study, strain induced martensitic transformation is studied by in situ digital image correlation (DIC) in a scanning electron microscope. Digital image correlation at submicron length scales enables mapping of transformation strains with high confidence. These are correlated with electron backscatter diffraction (EBSD) prior to and post deformation process to get a comprehensive understanding of the strain induced transformation mechanism. The results are compared with mathematical models for enhanced prediction of strain induced martensitic phase transformation.