nils-herrmann/clean_scirepeval huggingface dataset:
Emergence of a novel mobile colistin resistance gene, mcr 8, in NDM producing Klebsiella pneumoniae
Venue: Emerging Microbes Infections
Year: 2018

The rapid increase in carbapenem resistance among gram negative bacteria has renewed focus on the importance of polymyxin antibiotics (colistin or polymyxin E) However, the recent emergence of plasmid mediated colistin resistance determinants (mcr 1, 2, 3, 4, 5, 6, and 7) especially mcr 1, in carbapenem resistant Enterobacteriaceae is a serious threat to global health. Here, we characterized a novel mobile colistin resistance gene, mcr 8, located on a transferrable 95,983 bp IncFII type plasmid in Klebsiella pneumoniae. The deduced amino acid sequence of MCR 8 showed 31.08% 30.26% 39.96% 37.85% 33.51% 30.43% and 37.46% identity to MCR 1, MCR 2, MCR 3, MCR 4, MCR 5, MCR 6, and MCR 7, respectively. Functional cloning indicated that the acquisition of the single mcr 8 gene significantly increased resistance to colistin in both Escherichia coli and K. pneumoniae. Notably, the coexistence of mcr 8 and the carbapenemase encoding gene blaNDM was confirmed in K. pneumoniae isolates of livestock origin. Moreover, BLASTn analysis of mcr 8 revealed that this gene was present in a colistin and carbapenem resistant K. pneumoniae strain isolated from the sputum of a patient with pneumonia syndrome in the respiratory intensive care unit of a Chinese hospital in 2016. These findings indicated that mcr 8 has existed for some time and has disseminated among K. pneumoniae of both animal and human origin, further increasing the public health burden of antimicrobial resistance.
Empirical Research on Growth of Listed Companies in Food Industry
Year: 2015

This study uses empirical research method to analyze the growth of listed food companies in order to provide a new analytical method for the theoretical study of company growth. Meanwhile, this study offers valuable references about strategic development decisions for the listed food companies as well. As one of the main components, the food industry supports the growth of China's national economy. This study focuses on the characteristics of food industry. It studies the relationship between capital structure and growth of listed companies in food industry through financial ratios and empirical approaches. Firstly, listed companies from the A share and none special treatment market in Shanghai and Shenzhen stock exchange within year 2011 to 2013 are chosen as research subjects. Next, nineteen test indexes, from seven factors, such as profitability, debt paying ability, operating capacity, cost management ability, development capacity, Marketing capability and innovation capacity, are selected to construct the enterprise appraisal model based on factor analysis. The dependent variables of the model are seven test indexes picked from aspects of profitability, debt paying ability and operating capacity. Finally, this study draws the conclusion that the listed food companies' profitability and debt paying ability have a negative relationship with the firms' growth ability. However, there is a positive relationship between their operating capacity and growth ability.
Direct high resolution resonant Raman scattering measurements of InAs quantum dot dynamic nuclear spin polarization states
Year: 2020

We report on the direct measurement of the electron spin splitting and the accompanying nuclear Overhauser (OH) field, and thus the underlying nuclear spin polarization (NSP) and fluctuation bandwidth, in a single InAs quantum dot under resonant excitation conditions with unprecedented spectral resolution. The electron spin splitting is measured directly via resonant spin flip single photon Raman scattering detected by superconducting nanowires to generate excitation emission energy maps. The observed two dimensional maps reveal an OH field that has a non linear dependence on excitation frequency. This study provides new insight into earlier reports of so called avoidance and tracking, showing two distinct NSP responses directly by the addition of a emission energy axis. The data show that the polarization processes depend on which electron spin state is optically driven, with surprising differences in the polarization fluctuations for each case: in one case, a stabilized field characterized by a single peaked distribution shifts monotonically with the laser excitation frequency resulting in a nearly constant optical interaction strength across a wide detuning range, while in the other case the previously reported avoidance behavior is actually the result of a nonlinear dependence on the laser excitation frequency near zero detuning leading to switching between two distinct mesoscopic nuclear spin states. The magnitude of the field, which is as large as 400 mT, is measured with sub 100 nuclear spin sensitivity. Stable/unstable points of the OH field distribution are observed, resulting from the non linear feedback loop in the electron trion nuclear system. Nuclear spin polarization state switching occurs between fields differing by 160 mT at least as fast as 25 ms. Control experiments indicate that the strain induced quadrupolar interaction may explain the measured OH fields.
A Switched View of Retinex: Deep Self Regularized Low Light Image Enhancement
Venue: Neurocomputing
Year: 2021

Self regularized low light image enhancement does not require any normal light image in training, thereby freeing from the chains on paired or unpaired low /normalimages. However, existing methods suffer color deviation and fail to generalize to various lighting conditions. This paper presents a novel self regularized method based on Retinex, which, inspired by HSV, preserves all colors (Hue, Saturation) and only integrates Retinex theory into brightness (Value) We build a reflectance estimation network by restricting the consistency of reflectances embedded in both the original and a novel random disturbed form of the brightness of the same scene. The generated reflectance, which is assumed to be irrelevant of illumination by Retinex, is treated as enhanced brightness. Our method is efficient as a low light image is decoupled into two subspaces, color and brightness, for better preservation and enhancement. Extensive experiments demonstrate that our method outperforms multiple state of the art algorithms qualitatively and quantitatively and adapts to more lighting conditions.
Credit card churn forecasting by logistic regression and decision tree
Venue: Expert Syst. Appl.
Year: 2011

In this paper, two data mining algorithms are applied to build a churn prediction model using credit card data collected from a real Chinese bank. The contribution of four variable categories: customer information, card information, risk information, and transaction activity information are examined. The paper analyzes a process of dealing with variables when data is obtained from a database instead of a survey. Instead of considering the all 135 variables into the model directly, it selects the certain variables from the perspective of not only correlation but also economic sense. In addition to the accuracy of analytic results, the paper designs a misclassification cost measurement by taking the two types error and the economic sense into account, which is more suitable to evaluate the credit card churn prediction model. The algorithms used in this study include logistic regression and decision tree which are proven mature and powerful classification algorithms. The test result shows that regression performs a little better than decision tree.
Do parents of obese children use ineffective parenting strategies?
Venue: Journal of child health care for professionals working with children in the hospital and community
Year: 2013

Research has shown mixed findings about the relationship between parenting style and child lifestyle outcomes. This paper describes a cross sectional study that aimed to clarify the relationship between ineffective parenting and childhood obesity by using multiple measures of child and family functioning. Sixty two families with an obese child (aged four to 11 years) were matched with 62 families with a healthy weight child on key sociodemographic variables. Significant differences were found on several measures, including general parenting style, domain specific parenting practices, and parenting self efficacy (d .53 to 1.96) Parents of obese children were more likely to use permissive and coercive discipline techniques, and to lack confidence in managing children's lifestyle behaviour. In contrast, parents of healthy weight children were more likely to implement specific strategies for promoting a healthy lifestyle.
Generational change in gender gaps in political behaviour and attitudes: the roles of modernisation, secularisation and socialisation
Year: 2017

This thesis examines to what extent there are generational differences in gender gaps in political behaviour and attitudes, and what explains this generational variation. Generations differ considerably on factors such as women's role in the family and the workplace, gender inequality, and formative experiences, and I argue this leads to different gender gaps for different generations. I examine such generational variation in gender gaps in vote choice, left right self placement, attitudes towards spending and redistribution, and attitudes towards gender egalitarianism. Broad cross national trends in Europe and Canada are identified, as well as country specific patterns using Britain and the US as case studies. This thesis finds that generally, in the countries studied, men are more left wing than women in older birth cohorts, whilst women are more left wing than men in younger birth cohorts. This 'gender generation gap' is produced through processes of modernisation, especially secularisation. In addition to this broad trend, the political context or zeitgeist during a generation's formative years produces gender gaps in both vote choice and attitudes that differ between generations according to this socialisation experience. The influences of modernisation and such political socialisation interact to create complex patterns of generational variation in political gender gaps that differ across political contexts. For example, in the British case, women of younger cohorts are not more left wing in their vote choice than men. These results suggest that we should focus on gender gaps at the level of generational subgroups in order to fully understand political differences between men and women. Furthermore, they predict that gradually, the gender gap where women are more left wing than men will grow over time through generational replacement. However, they also indicate that this will not occur in all contexts, and that more work needs to be done to understand how the political context shapes gender gaps.
On ASER Performance of M ary QAM Schemes Over DF Coordinated NOMA
Venue: 2020 IEEE REGION 10 CONFERENCE (TENCON)
Year: 2020

In this work, we investigate the performance of a power domain based downlink decode and forward coordinated non orthogonal multiple access (NOMA) system with two users and one relay node. The system is considered to operate in half duplex mode over a Rayleigh faded channel, where the weak user is heavily shadowed with no direct link. Framework for average symbol rate error (ASER) and asymptotic ASER of generalized M ary QAM for both weak and strong user is developed. Further, the impact of various power levels over different QAM schemes for the users is analyzed. Furthermore, the impact of symmetric and asymmetric constellations for the users is also considered and their impact over the system is detailed and useful insights drawn. The derived analytical expressions are verified through Monte Carlo simulations, and compared with non coordinated NOMA.
Tomato endophyte bacteria diversity under long term organic agricultural manipulation systems
Year: 2021

Differences were observed in both microbial structure and physicochemical properties were observed in the organic and conventional agricultural systems. However, very little is known about the diversity of endophytic bacterial communities in plants grown in separate manipulation systems. The goal of this work was to get a broader overview of the diversity and dynamics of the tomato endophytic bacteria in the different agricultural manipulation systems. The structure of endophyte bacterial communities under different growth periods (seeding stage, flowering stage, fruiting stage and harvesting stage) agricultural manipulation practices (organic and conventional systems) and organ type (root or stem) were explored by using 16Sr RNA gene profiling in this study. A total of 2,014,992 part 16 SRRNA gene sequences were obtained. These sequences revealed large scale functional taxonomy units (OTUs) That is, there are 648 different OTUS in libraries, and 96 OTUs are common. Tomato endophytes consisted mainly of four phylloxera, of which Proteobacteria was the most represented, followed by Formigiotes, bacteriophages, Actinobacteria, Gamma proteobacteria, the most abundant class of proteobacteria, bacteriobacteria, and so on. Proteobacteria are low. General enterobacteriaceae, Vecella, Bacillus, Mesorizobium and Chrysobacterium were shared by all growth periods. Rich endophytic bacterial diversity was observed at the seedling stage (DI) and endophytic bacterial diversity at the flowering stage and fruiting stage was low. Significant difference in endophytic bacterial communities emerged from roots and different host biographical stages, and tomato exerts greater influence on endophyte bacteria compared to organ type (main) agricultural manipulation methods. Tomato endophyte microbiota have a distinct structure in different growth periods. Bacillus were enriched seeding stage and decreased in the fruiting stage while Mesorhizobium increased during in the fruiting stage. Tomato have distinct endosphere microbiota by comparing beta diversity of microbiota in different growth periods, compared with the manipulation resume and organ type. And a strong correlation was observed between the structure of the microbiota in the whole dataset and soil chemistry which indicated that the soil type and treatment affected the endosphere microbiota of tomato. Organ type (niche) exert more influence on the tomato microbiota compared with manipulation treatments between organic farming and conventional farming.
Literacy Teaching and Bilingualism in Visual Learning Processes: Contributions from Deaf Epistemologies
Year: 2014

Reading/writing teaching and learning processes for deaf students enrolled in bilingual contexts are examined in this study. The objective is to contribute to theoretical and practical reflection on the educational answers to the demands of literacy teaching for this particular population. The type of education now being offered in Chile and in other Latin American countries does not respond to the linguistic, cultural and learning characteristics of the deaf. In other words, current educational proposals do not consider the features of visual learning, the incorporation of sign language in learning processes, nor the cultural beliefs and modes of the deaf. A model of bilingual intercultural education (IBE) that is based on deaf epistemologies, values the language and culture of deaf communities, and inspires models for literacy instruction and teaching practices centered on the features of visual learning by the deaf is needed.
Binary Particle Agent Swarm Optimization For Best Fit Feature Weight Selection In Multi Document Text Summarization
Year: 2020

Auto Text summarization is the process through which a compressed version of the original text is created. Extraction based approaches are the one through which the important sentences and information are extracted from a single or multi document. This method select the sentences based on the calculation of scores for each sentence. The process of extracting sentences that are most important is a trivial task for the text mining researchers. The previous works concentrated on the automatic extraction of features through weighted TF IDF and dimension reduction using the centroid based k means clustering. This paper is intended to apply the extractive methods for automatic text summari9zation in multi document level using the swarm intelligence. Particle swarm optimization is used in this research for identifying the optimal weight of the feature scores for differentiating between the relevant and irrelevant features. The ROUGE tool is used for the evaluation. The student data provided by the educational department of US is considered for the experiment. The summary that are generated using the proposed methodology is compared to that of other methods such as Genetic Algorithm and Ant Colony Optimization and used the TF IDF: CBC as the benchmark method. The results from the experiment proves that the extracted summaries produced after the integration of the proposed method into TF IDF: CBC are performs better.
Fear of Falling Fear of Fading: The Emotional Dynamics of Positional and Personalised Individualism
Year: 2018

This article explores the emotional dynamics involved in the shaping of middle class subjectivities, aiming to move beyond the 'fear of falling' thesis and the attendant emphasis on the quest for positional advantage. I argue that this thesis offers a one dimensional notion of what it is that drives middle class perceptions, values and motivations, unsuitable for explaining current tensions between symbolic and economic fractions. Drawing on a comparative narrative interview study of managerial and professional parents in Norway, I describe the emotionally charged investments 'fear of falling' and 'fear of fading' as well as the excitements that drive different modes of socialisation in these groups. Further developing the phenomenological and psychosocial trajectories in Bourdieu's practice theory yields a productive tool for exploring these modes of socialisation, contributing to an enhanced conception of emotional dynamics of different middle class subjectivities.
A Review Paper on Providing Security in Cloud Computing Using User Behavior Profiling and Decoy Technology
Year: 2019

1Syed Asiya, 2N.Veda, 3B.Maheshwari 1Assistant Professor, 2,3Students Department of Computer Science Engineering Balaji Institute of Technology Sciences, Narsampet, India.  The new advancements in the field of data innovation offered the general population pleasure, solaces, and accommodation; however there are numerous security related issues. One of them is secret word record. Secret phrase documents have a great deal of security issue that has influenced a huge number of clients and also numerous organizations. The Cloud is the bunch of a PC associated with store data. Cloud computing makes achievable for numerous clients to, share normal processing assets, and to access and store their own and business data. Substantial information put away on cloud so, executing security turn out to be extremely vital on the customer side. Existing calculation bombed once key is lost by proprietor. User behavior profiling and decoy technology give an alternate method to anchor information on a server which is progressively effective and secure. There are numerous calculations on client conduct profiling and imitation innovation however nobody can discover the issue is that proficiently conveying the fake document in such a way the gatecrasher not ready to perceive the contrast between the authentic and fake record, when the mysterious conduct of the client recognized. Proposed a system which consists of user behavior profiling and decoy technology.
Does Firm Size Affect The Firm Profitability? Evidence from Turkey
Year: 2013

The aim of this study is to investigate the affect of firm size on profitability. In this study, data of 200 companies which were active in Istanbul Stock Exchange (ISE) between the years 2008 2011 has been used. "Return on Assets" (ROA) has been used as indicators of firm profitability and total assets, total sales and number of employees have been used as indicators of size. Multiple regression and correlation methods have been used in empirical analyses. The result of analysis indicates a positive relation between size indicators and profitability of firms. Control variables as the age of the firms and leverage rate have been found in a negative relation with ROA, but liquidity rate and ROA have been determined to have a positive relation.  Firm size, Profitability, Firm Performance, Turkey
MeanSum: A Neural Model for Unsupervised Multi Document Abstractive Summarization
Venue: ICML
Year: 2019

Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some progress has been made in learning sequence to sequence mappings with only unpaired examples. In our work, we consider the setting where there are only documents (product or business reviews) with no summaries provided, and propose an end to end, neural model architecture to perform unsupervised abstractive summarization. Our proposed model consists of an auto encoder where the mean of the representations of the input reviews decodes to a reasonable summary review while not relying on any review specific features. We consider variants of the proposed architecture and perform an ablation study to show the importance of specific components. We show through automated metrics and human evaluation that the generated summaries are highly abstractive, fluent, relevant, and representative of the average sentiment of the input reviews. Finally, we collect a reference evaluation dataset and show that our model outperforms a strong extractive baseline.
Towards artificial general intelligence with hybrid Tianjic chip architecture
Venue: Nature
Year: 2019

There are two general approaches to developing artificial general intelligence (AGI)1: computer science oriented and neuroscience oriented. Because of the fundamental differences in their formulations and coding schemes, these two approaches rely on distinct and incompatible platforms2 8, retarding the development of AGI. A general platform that could support the prevailing computer science based artificial neural networks as well as neuroscience inspired models and algorithms is highly desirable. Here we present the Tianjic chip, which integrates the two approaches to provide a hybrid, synergistic platform. The Tianjic chip adopts a many core architecture, reconfigurable building blocks and a streamlined dataflow with hybrid coding schemes, and can not only accommodate computer science based machine learning algorithms, but also easily implement brain inspired circuits and several coding schemes. Using just one chip, we demonstrate the simultaneous processing of versatile algorithms and models in an unmanned bicycle system, realizing real time object detection, tracking, voice control, obstacle avoidance and balance control. Our study is expected to stimulate AGI development by paving the way to more generalized hardware platforms. The 'Tianjic' hybrid electronic chip combines neuroscience oriented and computer science oriented approaches to artificial general intelligence, demonstrated by controlling an unmanned bicycle.