nils-herrmann/clean_scirepeval huggingface dataset:
Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications
Venue: J. Biomed. Semant.
Year: 2014

BackgroundScientific publications are documentary representations of defeasible arguments, supported by data and repeatable methods. They are the essential mediating artifacts in the ecosystem of scientific communications. The institutional "goal" of science is publishing results. The linear document publication format, dating from 1665, has survived transition to the Web.Intractable publication volumes; the difficulty of verifying evidence; and observed problems in evidence and citation chains suggest a need for a web friendly and machine tractable model of scientific publications. This model should support: digital summarization, evidence examination, challenge, verification and remix, and incremental adoption. Such a model must be capable of expressing a broad spectrum of representational complexity, ranging from minimal to maximal forms.ResultsThe micropublications semantic model of scientific argument and evidence provides these features. Micropublications support natural language statements; data; methods and materials specifications; discussion and commentary; challenge and disagreement; as well as allowing many kinds of statement formalization.The minimal form of a micropublication is a statement with its attribution. The maximal form is a statement with its complete supporting argument, consisting of all relevant evidence, interpretations, discussion and challenges brought forward in support of or opposition to it. Micropublications may be formalized and serialized in multiple ways, including in RDF. They may be added to publications as stand off metadata.An OWL 2 vocabulary for micropublications is available at http:/purl.org/mp. A discussion of this vocabulary along with RDF examples from the case studies, appears as OWL Vocabulary and RDF Examples in Additional file1.ConclusionMicropublications, because they model evidence and allow qualified, nuanced assertions, can play essential roles in the scientific communications ecosystem in places where simpler, formalized and purely statement based models, such as the nanopublications model, will not be sufficient. At the same time they will add significant value to, and are intentionally compatible with, statement based formalizations.We suggest that micropublications, generated by useful software tools supporting such activities as writing, editing, reviewing, and discussion, will be of great value in improving the quality and tractability of biomedical communications.
New Ways In Teaching Speaking
Year: 2016

Thank you very much for reading new ways in teaching speaking. As you may know, people have look hundreds times for their chosen readings like this new ways in teaching speaking, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some harmful virus inside their desktop computer. new ways in teaching speaking is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the new ways in teaching speaking is universally compatible with any devices to read.
Diversity and Structure of Bacterial Communities in the Gut of Spider: Thomisidae and Oxyopidae
Venue: Frontiers in Ecology and Evolution
Year: 2020

The gut microbiomes of insects were known to have great impact on their physiological properties for survival like nutrition, behavior, and health. In nature, spiders are one of the main predators of insects, and yet their gut microbiomes remain unclear. It is important to explore the gut microbiomes of spiders in the wild to gain an insight on the host bacterial relationship. Here, we studied the diversity and structure of gut bacterial communities of seven spider species belonging to two families, i.e. Thomisidae and Oxyopidae, from different states of India. Our data revealed a total of 16 bacterial phyla with Proteobacteria as the predominant group in Thomisidae and Firmicutes in Oxyopidae. The core bacterial communities in the spider guts include the genera of Acinetobacter, Staphylococcus, Corynebacterium, Cutibacterium, and Pseudomonas. The genus Paraclostridium was observed for the first time and only in one spider species, i.e. Peucetia viridans. Our data also indicated a higher gut bacterial community similarity between spider species belonging to Thomisidae as compared to those belonging to Oxyopidae bacteria. Furthermore, PICRUSt2 analysis predicted the presence of nine active functional metabolic pathways related to the metabolism of fatty acids and sugar, degradation of organic compounds, and biosynthesis of vitamin E.
Spectral analysis of non commutative harmonic oscillators: The lowest eigenvalue and no crossing
Year: 2013

The lowest eigenvalue of non commutative harmonic oscillators Q a b a 0 b 0 a b 1 is studied. It is shown that Q a b can be decomposed into four self adjoint operators, Q a b s p 1 2 Q s p and all the eigenvalues of each operator Q s p are simple. We show that the lowest eigenvalue of Q a b is simple whenever a b Furthermore a Jacobi matrix representation of Q s p is given and spectrum of Q s p is considered numerically.
Cokriging and multivariate kriging methods based on data of a functional random field
Year: 2017

Kriging and cokriging and their several related versions are techniques widely known and used in spatial data analysis. However, when the spatial data are functions a bridge between functional data analysis and geostatistics has to be built. We give an overview to cokriging analysis and multivariable spatial prediction to the case where the observations at each sampling location consist of samples of random functions. We extend multivariable geostatistical methods to the functional context. Our cokriging method predicts one variable at a time as in a classical multivariable sense, but considering as auxiliary information curves instead of vectors. We also give an extension of multivariable kriging to the functional context where is defined a predictor of a whole curve based on samples of curves located at a neighborhood of the prediction site. In both cases a non parametric approach based on basis function expansion is used to estimate the parameters, and we prove that both proposals coincide when using such an approach. A linear model of coregionalization is used to define the spatial dependence among the coefficients of the basis functions, and therefore for estimating the functional parameters. As an illustration the methodological proposals are applied to analyze two real data sets corresponding to average daily temperatures measured at 35 weather stations located in the Canadian Maritime Provinces, and penetration resistance data collected at 32 sampling sites of an experimental plot.
Introduction to statistical relational learning
Year: 2007

Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity relationship models as well as logic based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.Lise Getoor is Assistant Professor in the Department of Computer Science at the University of Maryland. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania.
On the relevance of double tax treaties
Venue: International tax and public finance
Year: 2020

This paper investigates the effects of double tax treaties (DTTs) on foreign direct investment (FDI) after controlling for their relevance in the presence of treaty shopping. DTTs cannot be considered a bilateral issue, but must be viewed as a network. We define tax distance as the cost of channelling corporate income from one country to another and, by considering treaty shopping through intermediate jurisdictions, we calculate the shortest (i.e. the cheapest) distance between any two countries. We show that relevant tax treaties which reduce the direct tax distance both over domestic law and the entire existing treaty network will increase FDI by about 18%
Improved Facial Expression Recognition Based on DWT Feature for Deep CNN
Venue: Electronics
Year: 2019

Facial expression recognition (FER) has become one of the most important fields of research in pattern recognition. In this paper, we propose a method for the identification of facial expressions of people through their emotions. Being robust against illumination changes, this method combines four steps: Viola Jones face detection algorithm, facial image enhancement using contrast limited adaptive histogram equalization (CLAHE) algorithm, the discrete wavelet transform (DWT) and deep convolutional neural network (CNN) We have used Viola Jones to locate the face and facial parts; the facial image is enhanced using CLAHE; then facial features extraction is done using DWT; and finally, the extracted features are used directly to train the CNN network, for the purpose of classifying the facial expressions. Our experimental work was performed on the CK+ database and JAFFE face database. The results obtained using this network were 96.46% and 98.43% respectively.
Evaluating contract agency performance in achieving reunification
Year: 2009

This paper analyzes the performance of contract agencies in achieving timely permanency for children in foster care. Using the Cox regression model on administrative data of children admitted in three years, we examine the hazard rate of family reunification for each agency both relative to its peers and compared with its own performance in previous years. This is achieved by introducing an interaction term between the agency dummy and the entry year indicators. Doing this provides a dynamic and comprehensive view of an agency's performance both over time and in relation to other agencies in the same locale. We believe that our modeling technique can serve as an effective tool for child welfare administrators to evaluate the performance of foster care service providers.
Digital pathology and artificial intelligence.
Venue: The Lancet. Oncology
Year: 2019

In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.
A systematic review of clinical trial registration in major respiratory journals 2010 2018
Venue: European Respiratory Journal
Year: 2018

The results of randomised clinical trials are regarded as the highest level of evidence for informing clinical guidelines and clinical practice [1, 2] The robustness of conduct and reporting of the results of randomised clinical trials is therefore of primary importance. To improve the transparency and quality of reporting of clinical trials, the International Committee of Medical Journal Editors (ICMJE) endorsed a policy on mandatory registration of clinical trials, which came into effect in July 2005  The trial registration recommendations outlined by the ICMJE state that a trial must be registered in a publicly accessible registry, before the enrolment of the first study participants, in order to be considered for publication. This policy is designed to reduce publication bias, prevent selective reporting of desirable or non reporting of undesirable results, and reduce research waste by making the research community aware of what questions are already being addressed by active trials. Retrospective trial registration remains common in respiratory clinical trials, and may be associated with reporting bias http:/ow.ly/S7p230m5kqV
Critical thinking, problem solving strategies and individual development assessment among NEETs research conducted in Slovakia, Poland and Estonia
Year: 2018

The paper presents the initial results of research into the individual conditions of the psychosocial functioning of NEETs in the educational context. The study was performed between May and September 2017 in Slovakia, Poland and Estonia, by means of a triangulation of quantitative tools. The general objective of the study is to diagnose the level of critical thinking (Cornell Critical Thinking) reasoning (T.I.P. problem solving strategies (NCS) and individual development assessment (IDEA) in a group of NEETs, and to compare these variables with one another and with socio demographic characteristics. Based on an initial analysis of the results (100 individuals from each country were invited to participate in the survey; 235 questionnaires were gathered) we can see that the group analysed presents a relatively high level of critical thinking and reasoning (intelligence indicator) and an average level of problem solving attitude. Breaking stereotypes about NEETs, the study fits into the critical paradigm of andragogical research. The results served as the foundation for a series of postulates regarding educational programmes based on andragogical assumptions.
A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition
Venue: Sensors
Year: 2019

Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68% which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.
Ethnocentrism "Made in Tags" go Hand in Hand for Indian Youths "Cetscale Validation and Dimensionality Assessment"
Year: 2013

"Consumer Ethnocentrism is the meticulousness, indeed morality of purchasing foreign manufactured products" Transnational economy creates a great pressure on marketers by mounting tough competition across the world, providing an opportunity to Indian customers to freely mobilize and have free access to overseas goods/services. In these circumstances, the topic of consumers' ethnocentric penchant can't be neglected. Improving the understanding of consumer's ethnocentric belief, attitude and proclivity towards 'in group' and 'out group' product may help marketers to comprehend consumer's purchase behavior and consequently maneuver strategic plans. However it is worth noting that the scale universally used to measure the construct is valid or not for Indian Consumers. This research addresses such an issue by validating CETSCALE in India. Data were collected from youths (Management graduates) by personally administrating the questionnaire. Analysis was done using both the latest tools that is exploratory and confirmatory factor analysis; dimensionality was assessed using causal relation with impulsiveness. Findings show that ethnocentrism of Indian consumers is multidimensional and in a stage of transition, within the country bounds result are consistent with earlier research and contrary to commonly held belief for Indian youth, ethnocentrism and favorable belief about foreign product go hand in hand. Key Words Ethnocentrism, Cetscale, Validation, Dimensionality, Reliability.
Generated Loss, Augmented Training, and Multiscale VAE
Venue: ArXiv
Year: 2019

The variational autoencoder (VAE) framework remains a popular option for training unsupervised generative models, especially for discrete data where generative adversarial networks (GANs) require workaround to create gradient for the generator. In our work modeling US postal addresses, we show that our discrete VAE with tree recursive architecture demonstrates limited capability of capturing field correlations within structured data, even after overcoming the challenge of posterior collapse with scheduled sampling and tuning of the KL divergence weight \beta$ Worse, VAE seems to have difficulty mapping its generated samples to the latent space, as their VAE loss lags behind or even increases during the training process. Motivated by this observation, we show that augmenting training data with generated variants (augmented training) and training a VAE with multiple values of \beta$ simultaneously (multiscale VAE) both improve the generation quality of VAE. Despite their differences in motivation and emphasis, we show that augmented training and multiscale VAE are actually connected and have similar effects on the model.
Analysis of MrsP Protocol in RTEMS Operating System
Year: 2019

The technological world is becoming increasingly familiarized with the usage of multiprocessing platforms on several types of devices. The capabilities provided by multiprocessor systems allow for the increase of the overall system 19s performance, enabling the execution of several tasks in a concurrent and parallel way. While this behaviour is beneficial for most systems, it has to be handled with care in the real time systems domain. Real time systems are well known for their challenging requirements, the most important of them being the need to guarantee that operations are completed within a given deadline. Thus, several efforts are being made in order to be possible to execute them in multiprocessor environments. However, there are several barriers that make the transition difficult, notably at the level of resource sharing mechanisms and process scheduling on a system.One of such efforts is the added support brought by the inclusion of several scheduling and task synchronization mechanisms to RTEMS (Real time Executive for Multiprocessor Systems) an open source real time operating system widely used by the space industry worldwide.RTEMS supports two multiprocessor shared resource protocols, OMIP (O(m) Independence Preserving Protocol) and MrsP (Multiprocessor Resource Sharing Protocol) From these two, MrsP is considered rather promising, largely covering the requirements imposed by real time multiprocessing systems. This protocol was successfully implemented in RTEMS but recently it was subject to some refinements in order to cover some flaws on its initial proposal.In the project presented in this report, the MrsP protocol is analysed from a theoretical point of view and this view is compared against the implementation of the protocol on RTEMS. The goal is to evaluate the correctness of the implementation, according to the proposed requirements. Additionally, a testbed is developed to test the protocol in its integrity in order to serve as a regression test platform and in some cases, where it makes sense, comparisons with the OMIP protocol are established. Analysis of MrsP Protocol in RTEMS Operating System CISTER
SINGLE MOLECULE STUDIES OF RAD4 RAD23 REVEAL A DYNAMIC DNA DAMAGE RECOGNITION PROCESS
Year: 2017

Nucleotide excision repair (NER) is an evolutionarily conserved mechanism that processes helix destabilizing and/or distorting DNA lesions, such as UV induced photoproducts. As the first step towards productive repair, the human NER damage sensor XPC RAD23B needs to efficiently locate sites of damage among billons of base pairs of undamaged DNA. In this dissertation, we investigated the dynamic protein DNA interactions during the damage recognition step using a combination of fluorescence based single molecule DNA tightrope assays, atomic force microscopy, as well as cell survival and in vivo repair kinetics assays. We observed that quantum dot labeled Rad4 Rad23, the yeast homolog of human XPC RAD23B, formed nonmotile complexes on DNA or conducted a one dimensional search via either random diffusion or constrained motion along DNA. Using atomic force microscopy, we studied binding of Rad4 lacking the b hairpin domain 3 (BHD3) to damage containing DNA and found that this structural motif is non essential for damage specific binding or DNA bending. Furthermore, we demonstrated that deletion of seven residues in the tip of b hairpin in BHD3 increased Rad4 Rad23 constrained motion at the expense of stable binding at sites of DNA lesions, without diminishing cellular UV resistance or photoproduct repair in vivo. These results suggest a distinct intermediate in the damage recognition process during NER, allowing dynamic DNA damage detection at a distance. Finally, we explore existing physical models and examples of subdiffusive motion, and discuss a model in which constrained motion by Rad4 Rad23 on DNA may be driven by conformational changes of the protein.