nils-herrmann/clean_scirepeval huggingface dataset:
Effectiveness of a home based therapeutic exercise program on lower back pain and functionality in Sickle Cell Disease (SCD) patients
Venue: Hematology, transfusion and cell therapy
Year: 2020

To investigate the effectiveness of a home based therapeutic exercise program on lower back pain and functionality of SCD patients.  A Hematology and Transfusion Medicine Center, University of Campinas (HEMOCENTRO UNICAMP)  This was a prospective study, with a three month follow up of SCD patients with lower back pain. The lumbar spine functionality was evaluated by questionnaires, trunk flexion and extension analyses by fiber optic electrogoniometry and measurements of muscle strength of trunk flexor and extensors. The Intervention Group (IG) comprised 18 volunteers, median age 44y (28 58) and the control group (CG) comprised 15 volunteers, median age 42y (19 58) who did not perform exercises. The protocol consisted of daily home based exercises with two evaluations: at the beginning and end of a three month program. In order to compare the groups at baseline, the Fisher's exact test and Mann Whitney test were used for categorical and numeric variables, respectively. The Wilcoxon test was used for related samples comparing numeric measures of each group over time with a 5% (p 0.05) significance level.  After the intervention, patients demonstrated a significant improvement, according to the Visual Analog Scale (VAS; p 0.01) Rolland Morris Disability questionnaire (RMDQ; p 0.01) and trunk flexion and extension muscle strength (p 0.01) No significant differences were found for the Start Back Screening Tool Brazil (SBST) and in measures of trunk flexion and extension range of motion (RoM)  Results suggest that daily home based exercises for a three month period ameliorate pain and improve disability related to lower back pain and muscle strength.
United Arab Emirates Mexico: Do's and dont's in negotiation strategies, "A Mexican perspective"
Year: 2014

Nowadays, globalization is a reality in every area of a human life; not only considering a lack of barriers between countries, but also when thinking about business.There are many studies about the important aspects when negotiating worldwide, mainlyin regards of differences in culture.Being successful in a negotiation requires the ability to develop special challenges and deal with the "unknown".Even for people with experience in cross cultural communication,the differences in lifestyles may become in wrong practices during international business negotiations.Skilled negotiators have the ability to analyze each situation, taking advantage of the circumstances for a mutual benefit.Along with these skills, it is important to considerforeign bureaucracies, and the completebusiness process to achieve the previously stated objectives (International negotiations,Cross cultural communication skills for international business executives, 2014) This paper studies the main differences between two nations: United Arab Emirates and Mexico.Since they are very different cultures, the aim of this research is to help all the people who are thinking about starting a new business in any of these countries, by describing and analyzing some highlighted areas of these markets; such as history, geography, government, religion, food, tourism and language. This paper will present some insights of both cultures according to the Hofstede Model, as well as the do's and dont'sthat negotiators must take into consideration when willing to reach a good deal abroad.
IL 33 regulates the IgA microbiota axis to restrain IL 1a dependent colitis and tumorigenesis.
Venue: The Journal of clinical investigation
Year: 2016

Inflammatory bowel diseases (IBD) affect over 5 million individuals in the industrialized world, with an increasing incidence rate worldwide. IBD also predisposes affected individuals to development of colorectal cancer, which is a leading cause of cancer related deaths in adults. Mutations in genes encoding molecules in the IL 33 signaling pathway are associated with colitis and colitis associated cancer (CAC) but how IL 33 modulates gut homeostasis is unclear. Here, we have shown that Il33 deficient mice are highly susceptible to colitis and CAC. Mechanistically, we observed that IL 33 promoted IgA production from B cells, which is important for maintaining microbial homeostasis in the intestine. Il33 deficient mice developed a dysbiotic microbiota that was characterized by increased levels of mucolytic and colitogenic bacteria. In response to chemically induced colitis, this microbial landscape promoted the release of IL 1a, which acted as a critical driver of colitis and CAC. Consequently, reconstitution of symbiotic microbiota or IL 1a ablation markedly ameliorated colitis susceptibility in Il33 deficient animals. Our results demonstrate that IL 33 promotes IgA production to maintain gut microbial homoeostasis and restrain IL 1a dependent colitis and CAC. This study therefore highlights modulation of IL 33, IgA, IL 1a, and the microbiota as a potential therapeutic approach in the treatment of IBD and CAC.
How can Automated Machine Learning Help Business Data Science Teams?
Venue: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
Year: 2019

Artificial intelligence and machine learning have attracted the attention of many commercial and non profit organizations aiming to leverage advanced analytics, in order to provide a better service to their customers, increase their revenues through creating new or improving their existing internal processes, and better exploit their data by discovering complex hidden patterns. Such advanced solutions require data scientists with rare (and generally expensive) skill sets. Moreover, such solutions are often perceived as complex black boxes to decision makers. Automated machine learning tools aim to reduce the expertise gap between the technical teams and stakeholders involved in business data science projects, by reducing the amount of time and specialized skills required to generate predictive models. We systematically benchmarked five automated machine learning tools against seven supervised learning problems of a business nature. Our results suggest that such tools, in fully automated mode, must be used cautiously, only where predictive models support low impact decisions and do not need to be explainable, and only by data scientists capable to ensure that all phases of the data mining process have been performed adequately.
Shape aware Semi supervised 3D Semantic Segmentation for Medical Images
Venue: MICCAI
Year: 2020

Semi supervised learning has attracted much attention in medical image segmentation due to challenges in acquiring pixel wise image annotations, which is a crucial step for building high performance deep learning methods. Most existing semi supervised segmentation approaches either tend to neglect geometric constraint in object segments, leading to incomplete object coverage, or impose strong shape prior that requires extra alignment. In this work, we propose a novel shapeaware semi supervised segmentation strategy to leverage abundant unlabeled data and to enforce a geometric shape constraint on the segmentation output. To achieve this, we develop a multi task deep network that jointly predicts semantic segmentation and signed distance map(SDM) of object surfaces. During training, we introduce an adversarial loss between the predicted SDMs of labeled and unlabeled data so that our network is able to capture shape aware features more effectively. Experiments on the Atrial Segmentation Challenge dataset show that our method outperforms current state of the art approaches with improved shape estimation, which validates its efficacy. Code is available at this https URL.
Deforestation spillovers from oil palm sustainability certification
Year: 2020

Environmental policies that impose restrictions within one location may be undermined or reinforced by "spillover effects," the movement of actors, processes, or knowledge to other locations. Such spillovers are an important consideration in the design of interventions seeking to reduce commodity driven deforestation. In these settings, global markets and mobile actors can move deforestation and conservation behaviors over large distances, complicating efforts to measure and manage spillovers. Here we quantify forest loss and conservation spillovers from the Roundtable on Sustainable Palm Oil (RSPO) certification system in Indonesian Borneo (Kalimantan) We examine whether spillovers from certification are transmitted through corporate groups (i.e. to non certified, RSPO member held plantations) or local agricultural markets (i.e. to lands near certified mills) We find that, from 2009 to 2016, spillovers from RSPO certification reduced deforestation within Indonesia's forest estate, but increased deforestation in areas zoned for agricultural use. The private RSPO certification system has complemented public conservation by aligning de facto land cover with central government land zoning policy. Despite these benefits, aggregate avoided deforestation attributed to direct and spillover effects was statistically and substantively insignificant when compared to the total deforestation occurring inside all of Kalimantan's oil palm concessions. While certification has reduced illegal deforestation, stronger sector wide action appears necessary to ensure that oil palm production is no longer a driver of forest loss.
Context aware Natural Language Generation with Recurrent Neural Networks
Venue: ArXiv
Year: 2016

This paper studied generating natural languages at particular contexts or situations. We proposed two novel approaches which encode the contexts into a continuous semantic representation and then decode the semantic representation into text sequences with recurrent neural networks. During decoding, the context information are attended through a gating mechanism, addressing the problem of long range dependency caused by lengthy sequences. We evaluate the effectiveness of the proposed approaches on user review data, in which rich contexts are available and two informative contexts, sentiments and products, are selected for evaluation. Experiments show that the fake reviews generated by our approaches are very natural. Results of fake review detection with human judges show that more than 50\ of the fake reviews are misclassified as the real reviews, and more than 90\ are misclassified by existing state of the art fake review detection algorithm.
Using scheelite residue and rice husk ash to manufacture lightweight aggregates
Year: 2021

Over the years, the use of expandable clays has supported the world production of lightweight aggregates (LWAs) however the gradual lack of natural resources requires using more sustainable practices. Thus, the aim is to develop LWAs suitable for use in structural lightweight concrete and other engineering services, using scheelite residue (SPR) and rice husk ash (RHA) Studies show that the production of these residues is increasing, their environmental impacts are significant, and their properties favour the development of LWAs. However, no research was found on the use of SPR in LWAs. Raw materials were characterised by laser granulometry, scanning electron microscopy, X ray fluorescence, X ray diffraction and simultaneous thermal analysis. Then, 23 mixtures were formulated, from the replacement of clay by up to 100% residues. The samples were sintered at 1100, 1150, 1200 and 1250 degC. The manufactured aggregates were characterised by the bloating index (BI) mass loss (LOI) particle density (rd) water absorption (WA24H) crushing strength (S) mineralogical composition and microstructure. Finally, the feasibility of using the manufactured LWAs was evaluated, based on a comparative analysis with the properties of a commercial LWA. The resulting aggregates showed: (BI) maximum of 77.44% (LOI) of up to 12.0% (rd) varying between 0.86 and 1.80 g/cm3, (WA24H) minimum of 2.0% and (S) up to 13.5 MPa. Overall, 42 specimens demonstrated properties at least similar to the commercial LWA ones. The results reveal that it is technically feasible to use SPR and RHA to produce LWAs.
Intelligent affect regression for bodily expressions using hybrid particle swarm optimization and adaptive ensembles
Venue: Expert Syst. Appl.
Year: 2015

We conduct dimensional affect recognition of bodily expressions.Hybrid particle swarm optimization (PSO) is proposed for feature selection.It mitigates premature convergence problem of conventional PSO.Mutation mechanisms of each subswarm work cooperatively to avoid stagnation.Our system outperforms other PSO based and bodily expression perception research. This research focuses on continuous dimensional affect recognition from bodily expressions using feature optimization and adaptive regression. Both static posture and dynamic motion bodily features are extracted in this research. A hybrid particle swarm optimization (PSO) algorithm is proposed for feature selection, which overcomes premature convergence and local optimum trap encountered by conventional PSO. It integrates diverse jump out mechanisms such as the genetic algorithm (GA) and mutation techniques of Gaussian, Cauchy and Levy distributions to balance well between convergence speed and swarm diversity, thus called GM PSO. The proposed PSO variant employs the subswarm concept and a cooperative strategy to enable mutation mechanisms of each subswarm, i.e. the GA and the probability distributions, to work in a collaborative manner to enhance the exploration and exploitation capability of the swarm leader, sustain the population diversity and guide the search toward an ultimate global optimum. An adaptive ensemble regression model is subsequently proposed to robustly map subjects' emotional states onto a continuous arousal valence affective space using the identified optimized feature subsets. This regression model also shows great adaption to newly arrived bodily expression patterns to deal with data stream regression. Empirical findings indicate that the proposed hybrid PSO optimization algorithm outperforms other state of the art PSO variants, conventional PSO and classic GA significantly in terms of catching global optimum and discriminative feature selection. The system achieves the best performance for the regression of arousal and valence when ensemble regression model is applied, in terms of both mean squared error (arousal: 0.054, valence: 0.08) and Pearson correlation coefficient (arousal: 0.97, valence: 0.91) and outperforms other state of the art PSO based optimization combined with ensemble regression and related bodily expression perception research by a significant margin.
Place des eaux minerales dans l'alimentation.
Year: 2006

RESUME La qualite des eaux consommees en France est globalement excellente. Toutefois, le consommateur doit etre clairement informe de leur composition et des mises en garde dont elles peuvent faire l'objet, ce qui est loin d'etre le cas aujourd'hui. On distingue, pour l'essentiel Les eaux minerales naturelles pre emballees, dont la purete naturelle est garantie, qui ont l'avantage d'un nom commercial specifique et d'une composition constante pour chaque source. Cependant, certaines d'entre elles comportent des concentrations d'ions trop elevees pour pouvoir etre consommees sans restriction, ce qui, parallelement, leur confere d'authentiques indications therapeutiques (eaux riches en calcium, en sulfates. Les eaux de source pre emballees, dont l'equilibre physico chimique est controle selon des normes, ce qui les met a l'abri de tout exces en mineraux. Cependant, ces eaux ont un nom commercial qui n'est pas specifique d'une source Les eaux de distribution publique, soumises apres traitement aux normes de potabilite (controles reguliers) qui peuvent etre bues toute une vie sans inconvenient. Des boissons supplementees ou aromatisees a base d'eau de source ou d'eau minerale regies par d'autres lois. Les eaux minerales naturelles distribuees en France presentent une grande richesse du fait de la diversite de leur origine et de leur composition, ce qui leur confere un large eventail d'utilisation. Il est preferable, pour la consommation familiale courante, d'utiliser une eau minerale naturelle peu mineralisee ou une eau de source, si l'on souhaite remplacer l'eau de distribution. Leur gout agreable en fait un precieux recours pour prevenir et traiter les pertes hydro electrolytiques (fievre, temperature exterieure elevee, traitement diuretique, diarrhee. notamment chez les personnes agees dont les besoins en eau sont eleves (1500 a 1700 mL/j) et qui doivent etre encouragees a boire lorsque leur sensation de soif est alteree. A la difference des eaux de source, dont le residu sec doit etre
Fuzzy modularity and fuzzy community structure in networks
Year: 2010

To find the fuzzy community structure in a complex network, in which each node has a certain probability of belonging to a certain community, is a hard problem and not yet satisfactorily solved over the past years. In this paper, an extension of modularity, the fuzzy modularity is proposed, which can provide a measure of goodness for the fuzzy community structure in networks. The simulated annealing strategy is used to maximize the fuzzy modularity function, associating with an alternating iteration based on our previous work. The proposed algorithm can efficiently identify the probabilities of each node belonging to different communities with random initial fuzzy partition during the cooling process. An appropriate number of communities can be automatically determined without any prior knowledge about the community structure. The computational results on several artificial and real world networks confirm the capability of the algorithm.
Sentiment Classification of Spanish Reviews: An Approach based on Feature Selection and Machine Learning Methods
Venue: J. Univers. Comput. Sci.
Year: 2016

Sentiment analysis aims to extract users' opinions from review documents. Nowadays, there are two main approaches for sentiment analysis: the semantic orientation and the machine learning. Sentiment analysis approaches based on Machine Learning (ML) methods work over a set of features extracted from the users' opinions. However, the high dimensionality of the feature vector reduces the effectiveness of this approach. In this sense, we propose a sentiment classification method based on feature selection mechanisms and ML methods. The present method uses a hybrid feature extraction method based on POS pattern and dependency parsing. The features obtained are enriched semantically through commonsense knowledge bases. Then, a feature selection method is applied to eliminate the noisy and irrelevant features. Finally, a set of classifiers is trained in order to classify unknown data. To prove the effectiveness of our approach, we have conducted an evaluation in the movies and technological products domains. Also, our proposal was compared with well known methods and algorithms used on the sentiment classification field. Our proposal obtained encouraging results based on the F measure metric, ranging from 0.786 to 0.898 for the aforementioned domains.
American Contact Dermatitis Society Core Allergen Series: 2017 Update.
Venue: Dermatitis contact, atopic, occupational, drug
Year: 2017

The American Contact Dermatitis Society Core Allergen Series was introduced in 2012. After 4 years of use, changes in our recommended allergens are necessary. For the updated series, we have reordered the first 4 panels to approximately mirror the current TRUE Test and removed parthenolide, triclosan, glutaraldehyde, and jasmine. Polymyxin B, lavender, sodium benzoate, ethylhexylglycerin, and benzoic acid are new additions to the American Contact Dermatitis Society series.
The Longitudinal Israeli Study of Twins (LIST) Reaches Adolescence: Genetic and Environmental Pathways to Social, Personality and Moral Development
Venue: Twin Research and Human Genetics
Year: 2019

focuses on the developmental, genetic and environmental contributions to individual differences in children's and adolescents' social behavior. Key variables have been empathy, prosocial behavior, temperament and values. Another major goal of LIST has been to study gene environment correlations, mainly concerning parenting. LIST includes 1657 families of Hebrew speaking Israeli twins who have participated at least once in the study. Children's environment and their development are assessed in a multivariate, multimethod fashion, including observed, parent reported and self reported data. The current article summarizes and updates recent findings from LIST. For example, LIST provided evidence for the heritability of human values with the youngest sample to date, and the first genetic investigation of adolescents' identity formation. Finally, future aims of LIST are discussed.
La memoria historica de las victimas en Colombia en espacios artisticos urbanos
Year: 2019

En las ciencias sociales los estudios en torno a la memoria tomaron auge hacia la decada de los ochenta pero quiza es despues del Acuerdo de paz cuando se masifican. Sin embargo, las victimas, desde el inicio encontraron en el arte una manera de narrar sus vivencias y gritos de no repeticion. En ese sentido lo que realmente pretende responder el texto es si el arte logra expresar las verdades y los hechos que vivieron las victimas, reflejando asi un cambio en sus practicas de cara a la reconciliacion y dejando a un lado la idealizacion desde lo estetico. Para aportar bases a la discusion entre memoria, arte y comunicacion, se realiza un breve panorama y concepto del arte, un acercamiento al concepto de memoria y finalmente se presenta la relacion entre arte, memoria y comunicacion, teniendo en cuenta algunas obras que se han destacado dentro de un espacio urbano determinado como Bogota.
A 06 Collegiate Student Athlete Experiences with COVID 19 and Attitudes About Returning to Sport
Venue: Archives of Clinical Neuropsychology
Year: 2021

When COVID 19 was declared a global pandemic, collegiate athletic departments faced the difficult decision to postpone or cancel sport activities. Aside from concerns of virus transmission, disruptions in sport impacted student athletes broadly. This study aimed to investigate athletes' experiences during the pandemic toward developing a better understanding of athletes' attitudes about the virus and returning to sport.  Participants included 245 varsity student athletes across NCAA sports and divisions who voluntarily completed an online survey between August and October 2020.  A positive COVID 19 result during the pandemic was reported by 1 in 4 athletes. Mental health, cancelled seasons, and academic progress were rated the top three COVID related concerns. Declines in physical fitness were reported by 50% of participants and change in weight was reported by 45% While agreement that their athletic department was taking COVID 19 seriously was endorsed by 78% of athletes, 26% believed the proposed safety procedures lacked feasibility. Concern about speaking up if the procedures were not followed was endorsed by 79% of athletes. Of the participants, 88% agreed that they have a social responsibility to prevent the spread of COVID 19. Only 44% worried about being exposed to the virus during sport participation. Comfort returning to sport immediately was endorsed by 70% of student athletes. Just over half said they would get vaccinated if the vaccine became available.  Student athletes have been affected by the pandemic in several ways. It is evident that athletes are concerned about the pandemic's impact on mental and physical health and are eager to return to sport.