nils-herrmann/clean_scirepeval huggingface dataset:
The Belmont Report
Year: 2014

The Belmont Report summarizes the basic ethical principles and guidelines identified by the commission to assist in the protection of human subjects in research. The report focuses on three main areas, (1) the boundaries between research and practice, (2) the basic ethical principles that should underlie the conduct of research (autonomy, beneficence, justice) and the protection of human subjects, and (3) the application of these principles into practice. The report outlines general recommendations regarding obtaining informed consent, the assessment of risk and benefit, and the recruitment of participants. The Belmont Report lays out many of the ethical considerations that, to this day, are used in formulating regulations and in ensuring the protection of human study volunteers.  belmont report; research ethics; human subjects; biomedical research
Fake News Detection
Year: 2018

Fake news, one of the biggest new age problems has the potential to mould opinions and influence decisions.The proliferation of fake news on social media and Internet is deceiving people to an extent which needs to be stopped.The existing systems are inefficient in giving a precise statistical rating for any given news claim. Also, the restrictions on input and category of news make it less varied. This paper proposes a system that classifies unreliable news into different categories after computing an F score. This system aims to use various NLP and classification techniques to help achieve maximum accuracy.
Propuesta de un modelo para la evaluacion de materiales virtuales para la ensenanza del noruego como lengua extranjera para hispanohablantes un diseno basado en el analisis de tres portales web mediante el uso del modelo MOSCA
Year: 2016

This document presents the final report on the research Project titled Virtual Materials Development Proposal for Teaching Norwegian as a Foreign Language to Spanish speakers. This paper offers the theoretical foundations for research, an assessment and analysis carried out on three websites conceived for Norwegian language learning. This is done to verify their quality; This study used an adapted version of the MOSCA Assessment Model (Systemic Model of Quality) created by Santovena (2005) for assessing technological tools.
Reliability of hunger related assessments during 24 hour fasts and their relationship to body composition and subsequent energy compensation
Venue: Physiology Behavior
Year: 2018

Many diets employ regular periods of fasting that extend beyond a typical overnight fast (i.e. intermittent fasting [IF] Evaluation of acute fasting responses provides information concerning the potential theoretical rationale for IF. The purpose of the present investigation was to assess the test retest reliability of hunger related variables during 24 hour fasts and the relationship between these variables and body composition, as well as subsequent energy intake (EI) after fasting. Eleven participants (6 F, 5 M) completed two 24 hour fasts after being provided a 3 day standardized weight maintenance diet. From 16 to 24 h of fasting, participants were directly observed and provided hourly assessments of hunger, desire to eat (DTE) prospective food consumption (PFC) fullness and energy. After the fast, participants were allowed ad libitum food consumption, and compensation was calculated as EI relative to weight maintenance energy needs. Test retest reliability for hunger related assessments at particular durations of fasting was evaluated using intraclass correlation coefficients (ICC) changes in dependent variables were evaluated using ANOVA with repeated measures, and relationships between variables were explored using bivariate correlations. At 16 h of fasting, the ICCs for all hunger related assessments were statistically significant (r 0.67 0.91; p 0.05) However, as the fast progressed, reliability varied substantially. When averaged across the nine measurements, the ICCs were: 0.81 (fullness) 0.74 (PFC) 0.67 (energy) 0.44 (DTE) and 0.36 (hunger) Body fat percentage was significantly correlated with changes in PFC (r 0.62, p 0.04) hunger (r 0.66, p 0.03) DTE (r 0.71, p 0.02) and fullness (r 0.63, p 0.04) but not energy (r 0.16, p 0.64) Average EI compensation was only 60% of weight maintenance needs, but substantial variability was observed (7 to 110% compensation) Compensation was significantly correlated with changes in PFC (r 0.72, p 0.01) hunger (r 0.63, p 0.04) and DTE (r 0.60, p 0.05) but not fullness (r 0.58, p 0.06) or energy (r 0.35, p 0.29) Compensation and body fat percentage were not correlated (r 0.03, p 0.94) The percent of energy intake from fat and protein increased after the fast (29.9 to 37.3% and 13.8 to 16.8% p 0.05) while the percent of energy intake from carbohydrate decreased (56.4 to 46.0% p 0.02) These results may have implications for IF programs. It is possible that the implementation of multiple "test fasts," in which subjective variables and subsequent energy intake are evaluated, could be used to identify candidates who may be more likely to benefit from an IF program.
Deep Metric Learning for Practical Person Re Identification
Venue: ArXiv
Year: 2014

Various hand crafted features and metric learning methods prevail in the field of person re identification. Compared to these methods, this paper proposes a more general way that can learn a similarity metric from image pixels directly. By using a "siamese" deep neural network, the proposed method can jointly learn the color feature, texture feature and metric in a unified framework. The network has a symmetry structure with two sub networks which are connected by Cosine function. To deal with the big variations of person images, binomial deviance is used to evaluate the cost between similarities and labels, which is proved to be robust to outliers. Compared to existing researches, a more practical setting is studied in the experiments that is training and test on different datasets (cross dataset person re identification) Both in "intra dataset" and "cross dataset" settings, the superiorities of the proposed method are illustrated on VIPeR and PRID.
Towards Natural Language Story Understanding with Rich Logical Schemas
Year: 2019

Generating "commonsense' knowledge for intelligent understanding and reasoning is a difficult, long standing problem, whose scale challenges the capacity of any approach driven primarily by human input. Furthermore, approaches based on mining statistically repetitive patterns fail to produce the rich representations humans acquire, and fall far short of human efficiency in inducing knowledge from text. The idea of our approach to this problem is to provide a learning system with a "head start" consisting of a semantic parser, some basic ontological knowledge, and most importantly, a small set of very general schemas about the kinds of patterns of events (often purposive, causal, or socially conventional) that even a one or two year old could reasonably be presumed to possess. We match these initial schemas to simple children's stories, obtaining concrete instances, and combining and abstracting these into new candidate schemas. Both the initial and generated schemas are specified using a rich, expressive logical form. While modern approaches to schema reasoning often only use slot and filler structures, this logical form allows us to specify complex relations and constraints over the slots. Though formal, the representations are language like, and as such readily relatable to NL text. The agents, objects, and other roles in the schemas are represented by typed variables, and the event variables can be related through partial temporal ordering and causal relations. To match natural language stories with existing schemas, we first parse the stories into an underspecified variant of the logical form used by the schemas, which is suitable for most concrete stories. We include a walkthrough of matching a children's story to these schemas and generating inferences from these matches.
Drug and Opioid Involved Overdose Deaths United States, 2013 2017
Venue: MMWR. Morbidity and mortality weekly report
Year: 2018

The 63,632 drug overdose deaths in the United States in 2016 represented a 21.4% increase from 2015; two thirds of these deaths involved an opioid (1) From 2015 to 2016, drug overdose deaths increased in all drug categories examined; the largest increase occurred among deaths involving synthetic opioids other than methadone (synthetic opioids) which includes illicitly manufactured fentanyl (IMF) (1) Since 2013, driven largely by IMF, including fentanyl analogs (2 4) the current wave of the opioid overdose epidemic has been marked by increases in deaths involving synthetic opioids. IMF has contributed to increases in overdose deaths, with geographic differences reported (1) CDC examined state level changes in death rates involving all drug overdoses in 50 states and the District of Columbia (DC) and those involving synthetic opioids in 20 states, during 2013 2017. In addition, changes in death rates from 2016 to 2017 involving all opioids and opioid subcategories, were examined by demographics, county urbanization levels, and by 34 states and DC. Among 70,237 drug overdose deaths in 2017, 47,600 (67.8% involved an opioid. From 2013 to 2017, drug overdose death rates increased in 35 of 50 states and DC, and significant increases in death rates involving synthetic opioids occurred in 15 of 20 states, likely driven by IMF (2,3) From 2016 to 2017, overdose deaths involving all opioids and synthetic opioids increased, but deaths involving prescription opioids and heroin remained stable. The opioid overdose epidemic continues to worsen and evolve because of the continuing increase in deaths involving synthetic opioids. Provisional data from 2018 indicate potential improvements in some drug overdose indicators;SS however, analysis of final data from 2018 is necessary for confirmation. More timely and comprehensive surveillance data are essential to inform efforts to prevent and respond to opioid overdoses; intensified prevention and response measures are urgently needed to curb deaths involving prescription and illicit opioids, specifically IMF.
Effect of isotretinoin on myopia and axial length: a pilot study
Venue: Cutaneous and ocular toxicology
Year: 2020

To investigate the effect of oral isotretinoin use on refractive error, axial length, and anteroposterior segment parameters.  In this prospective study, 50 eyes of 50 patients using isotretinoin with a diagnosis of acne vulgaris and 50 eyes of 50 healthy control subjects were included. After detailed biomicroscopy, measurements were taken of axial length, lens thickness, central corneal thickness, anterior chamber depth, central retinal thickness, and subfoveal choroidal thickness. The pupils of both eyes were dilated with one drop of cycloplegic drops after refraction measurement. Visual acuity examination was performed with a Snellen chart the next day. The same procedure was repeated at the end of the third and sixth month of drug treatment.  Forty seven patients with acne vulgaris and 45 healthy controls met the inclusion criteria and were included in the analysis. The mean ages of the patients and the controls were 21.7 2.5 years (range, 18 28 years) and 22.6 2.7 years (range, 19 27 years) respectively. No significant changes were observed in any parameters in the third and sixth month in the control group (p 0.05) The most important result was significant increases in myopia and axial length in the sixth month of isotretinoin use (p 0.01, p 0.04, respectively) There were no significant relationships between increases in myopia and axial length and patients' age, sex, drug dose, and initial refraction (p 0.05) The changes in spherical equivalent and axial length differed significantly between the drug group and the control group (p 0.001, p 0.001, respectively)  Isotretinoin is one of the important molecules in the aetiology of myopia. Oral isotretinoin treatment may increase myopia and axial length, although not to a clinically significant degree. However, as this was a pilot study, there is a need for further studies with more patients and longer follow up periods.
Understanding the role of individual units in a deep neural network
Venue: Proceedings of the National Academy of Sciences
Year: 2020

Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing.
Documento sobre la situacion del modelo espanol de Medicina Intensiva: Plan estrategico SEMICYUC 2018 2022
Year: 2019

Resumen El modelo espanol de Medicina Intensiva requiere una revision y reflexion continuas. Tenemos valores y fortalezas que se concretan en nuestra actividad y en el importante papel que realizamos a diario a nivel hospitalario. Otras especialidades pugnan por compartir actividades de cuidados del paciente critico y otras circunstancias pueden hacer mermar esa actividad. Este articulo es el reflejo de la reflexion de un importante numero de miembros del Grupo de Trabajo de Planificacion, Organizacion y Gestion de nuestra Sociedad (GTPOG SEMICYUC) Se afrontan las acciones que se deben llevar a cabo para la actualizacion del modelo de Medicina Intensiva, hacia un modelo mas abierto de UCI sin paredes o UCI extendida. Se aborda desde tres puntos de vista que deben ser complementarios: las acciones a nivel de la Administracion, las acciones a nivel de nuestra Sociedad Cientifica y las llevadas a cabo por cada uno de los Servicios (liderados por sus representantes) tanto a nivel hospitalario como a nivel de las autoridades sanitarias en cada comunidad autonoma.
Lambda Doubling Calculation of Carbon Monoxide C12O16 Molecule
Year: 2015

The theoretical emission spectrum of C 12 O 16 molecule, calculation of lambda doubling, the relation between wavenumbers R(J) Q(J) P(J) and rotational state energies F(J) F(J 1) with rotational quantum number J respectively have been investigated for A 1 p X 1 S and B 1 S X 1 S state using rotational, vibrational constants for both ground and excited states for vibrational transitions v" 0 to v' 0 to 9, using rotational levels J 0 up to J 20. The behavior of L doubling value with J is shown to be linear and L doubling values for A 1 p X 1 S transition greater than that of B 1 S X 1 S transition.  Emission spectrum, lambda doubling, rotational state energies, rotational quantum number Cite this Article Dhia Hamdi Al amiedy. Lambda doubling calculation of carbon monoxide C 12 O 16 molecule. Trends in Chemical Engineering. 2015; 2(2) 25 31p.
Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category
Venue: Front. Chem.
Year: 2018

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t Distributed Stochastic Neighbor Embedding (t SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF) support vector machine (SVM) artificial neural network (ANN) k nearest neighbors (kNN) and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease category(ies) or organ(s) of action of a molecule.
PruneNet: Channel Pruning via Global Importance
Venue: ArXiv
Year: 2020

Channel pruning is one of the predominant approaches for accelerating deep neural networks. Most existing pruning methods either train from scratch with a sparsity inducing term such as group lasso, or prune redundant channels in a pretrained network and then fine tune the network. Both strategies suffer from some limitations: the use of group lasso is computationally expensive, difficult to converge and often suffers from worse behavior due to the regularization bias. The methods that start with a pretrained network either prune channels uniformly across the layers or prune channels based on the basic statistics of the network parameters. These approaches either ignore the fact that some CNN layers are more redundant than others or fail to adequately identify the level of redundancy in different layers. In this work, we investigate a simple yet effective method for pruning channels based on a computationally light weight yet effective data driven optimization step that discovers the necessary width per layer. Experiments conducted on ILSVRC $12$ confirm effectiveness of our approach. With non uniform pruning across the layers on ResNet $50$ we are able to match the FLOP reduction of state of the art channel pruning results while achieving a $0.98\ higher accuracy. Further, we show that our pruned ResNet $50$ network outperforms ResNet $34$ and ResNet $18$ networks, and that our pruned ResNet $101$ outperforms ResNet $50$
Cross Cultural Conflicts in Not Without my Daughter
Year: 2016

This study aims to identify and analyze the cultural conflicts between the main characters in the novel Not Without my Daughter (NW) The analysis was carried out through the following process. The first procedure related to problems of classification i.e. cross cultural conflicts. The next phase of data analysis related to the colletion data of cross cultural conflicts in NW. The last phase is presentation the result of the analysis that had been conducted in this research. Having analyzed the data, the researcher concludes that cultural conflicts occured in NW because of stereotype, prejudice, and ethnocentrism. Cultural conflicts can be prevented if we increase our awareness of our own attitudes and learn to be sensitive to cross cultural differences. However, if we develop intercultural sensitivity, it does not mean that we need to lose our cultural identities but rather that we recognize cultural influences within ourselves and within others.
PhenoScanner V2: an expanded tool for searching human genotype phenotype associations
Venue: Bioinform.
Year: 2019

PhenoScanner is a curated database of publicly available results from large scale genetic association studies in humans. This online tool facilitates 'phenome scans' where genetic variants are cross referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner 'PhenoScanner V2' including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants. Availability and implementation PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.
Distributed Heavy Ball: A Generalization and Acceleration of First Order Methods With Gradient Tracking
Venue: IEEE Transactions on Automatic Control
Year: 2020

We study distributed optimization to minimize a sum of smooth and strongly convex functions. Recent work on this problem uses gradient tracking to achieve linear convergence to the exact global minimizer. However, a connection among different approaches has been unclear. In this paper, we first show that many of the existing first order algorithms are related with a simple state transformation, at the heart of which lies a recently introduced algorithm known as <inline formula><tex math notation="LaTeX"\mathcal {AB}/tex math>/inline formula> We then present <italic>distributed heavy ball</italic> denoted as <inline formula><tex math notation="LaTeX"\mathcal {AB}m$/tex math>/inline formula> that combines <inline formula><tex math notation="LaTeX"\mathcal {AB}/tex math>/inline formula> with a momentum term and uses nonidentical local step sizes. By simultaneously implementing both row and column stochastic weights, <inline formula><tex math notation="LaTeX"\mathcal {AB}m$/tex math>/inline formula> removes the conservatism in the related work due to doubly stochastic weights or eigenvector estimation. <inline formula><tex math notation="LaTeX"\mathcal {AB}m$/tex math>/inline formula> thus naturally leads to optimization and average consensus over both undirected and directed graphs. We show that <inline formula><tex math notation="LaTeX"\mathcal {AB}m$/tex math>/inline formula> has a global <inline formula><tex math notation="LaTeX"$R$/tex math>/inline formula> linear rate when the largest step size and momentum parameter are positive and sufficiently small. We numerically show that <inline formula><tex math notation="LaTeX"\mathcal {AB}m$/tex math>/inline formula> achieves acceleration, particularly when the objective functions are ill conditioned.
Evaluacion de los aprendizajes en lenguas extranjeras: hacia practicas justas y democraticas
Year: 2012

La falta de coherencia encontrada entre la evaluacion y la promocion en lenguas extranjeras se abordo en esta investigacion accion interinstitucional con la consolidacion de un Sistema consensuado de Evaluacion. La implementacion de dicho Sistema en cursos de ingles para principiantes en tres programas de extension para adultos, demostro que la variedad de tipos y formas de evaluacion, la rigurosidad y la sistematicidad, y el diseno meticuloso de instrumentos, formatos y tareas de evaluacion, transforman las practicas evaluativas en justas y democraticas, y benefician a estudiantes, profesores e instituciones.