nils-herrmann/clean_scirepeval huggingface dataset:
Interventional Effects of L arabinose on the Liver Damage of Type 2 Diabetic Rats
Year: 2013

To study the interventional effects of L arabinose on the liver damage of type 2 diabetic rats,type 2 diabetic rat models were established through the combination of the diet of high sugar fat with intravenous injection of small doses of streptozotocin(STZ).The weight and the index of the liver,the levels of fasting blood glucose(FBG),the activities of alanine transaminease(ALT) and aspartate transaminase(AST) were measured after four weeks of intragastric administration of L arabinose to observe the interventional effects of L arabinose on the liver damage of type 2 diabetic rats.The results show that L arabinose obviously improves the level of FBG,significantly decreases the activities of ALT and AST in the serum of diabetic rats.Furthermore,there is a strong positive correlation between the decreased degree and the dietary level. L arabinose can,to some extent,significantly relieve liver damage and protect the liver of diabetic rats.
Multiscale Approaches To Music Audio Feature Learning
Venue: ISMIR
Year: 2013

Content based music information retrieval tasks are typically solved with a two stage approach: features are extracted from music audio signals, and are then used as input to a regressor or classifier. These features can be engineered or learned from data. Although the former approach was dominant in the past, feature learning has started to receive more attention from the MIR community in recent years. Recent results in feature learning indicate that simple algorithms such as K means can be very effective, sometimes surpassing more complicated approaches based on restricted Boltzmann machines, autoencoders or sparse coding. Furthermore, there has been increased interest in multiscale representations of music audio recently. Such representations are more versatile because music audio exhibits structure on multiple timescales, which are relevant for different MIR tasks to varying degrees. We develop and compare three approaches to multiscale audio feature learning using the spherical K means algorithm. We evaluate them in an automatic tagging task and a similarity metric learning task on the Magnatagatune dataset.
Outlier detection for the National System of Costs Survey and Indices of Civil Construction (Sinapi) at The Brazilian Institute of Geography and Statistics (IBGE)
Year: 2019

Price indices collect periodically huge amounts of data for products in different geographic locations. Such datasets may contain outliers due to sampling and non sampling errors. The presence of outliers may bias the estimates and lead to misleading results. In such scenario, outlier detection techniques are very important to guarantee good estimator properties. The current methodology adopted in Sinapi relies on boxplot thresholds, a non stochastic approach, of two aggregated univariate analysis to decide whether a price is pointed as outlier or not. This work presents a new outlier detection methodology based on multivariate Mahalanobis distance. This approach takes the covariance matrix into consideration and requires that the price dataset follow approximately a multivariate normal distribution. In the approach derived here we obtain robust mean and covariances estimates adopting the "Passo R" algorithm. Furthermore, we show how to obtain normality of the price data by the use of the Lambert Way transformation, which is able to deal with skewness and kurtosis of prices distributions and provide good approximations for normality. The views expressed in this paper are those of the authors and do not necessarily reflect the views of IBGE.
BreathPrint: Breathing Acoustics based User Authentication
Venue: MobiSys
Year: 2017

We propose BreathPrint, a new behavioural biometric signature based on audio features derived from an individual's commonplace breathing gestures. Specifically, BreathPrint uses the audio signatures associated with the three individual gestures: sniff, normal, and deep breathing, which are sufficiently different across individuals. Using these three breathing gestures, we develop the processing pipeline that identifies users via the microphone sensor on smartphones and wearable devices. In BreathPrint, a user performs breathing gestures while holding the device very close to their nose. Using off the shelf hardware, we experimentally evaluate the BreathPrint prototype with 10 users, observed over seven days. We show that users can be authenticated reliably with an accuracy of over 94% for all the three breathing gestures in intra sessions and deep breathing gesture provides the best overall balance between true positives (successful authentication) and false positives (resiliency to directed impersonation and replay attacks) Moreover, we show that this breathing sound based biometric is also robust to some typical changes in both physiological and environmental context, and that it can be applied on multiple smartphone platforms. Early results suggest that breathing based biometrics show promise as either to be used as a secondary authentication modality in a multimodal biometric authentication system or as a user disambiguation technique for some daily lifestyle scenarios.
Hematoma growth is a determinant of mortality and poor outcome after intracerebral hemorrhage.
Venue: Neurology
Year: 2006

Although volume of intracerebral hemorrhage (ICH) is a predictor of mortality, it is unknown whether subsequent hematoma growth further increases the risk of death or poor functional outcome.  To determine if hematoma growth independently predicts poor outcome, the authors performed an individual meta analysis of patients with spontaneous ICH who had CT within 3 hours of onset and 24 hour follow up. Placebo patients were pooled from three trials investigating dosing, safety, and efficacy of rFVIIa (n 115) and 103 patients from the Cincinnati study (total 218) Other baseline factors included age, gender, blood glucose, blood pressure, Glasgow Coma Score (GCS) intraventricular hemorrhage (IVH) and location.  Overall, 72.9% of patients exhibited some degree of hematoma growth. Percentage hematoma growth (hazard ratio [HR] 1.05 per 10% increase [95% CI: 1.03, 1.08; p 0.0001] initial ICH volume (HR 1.01 per mL [95% CI: 1.00, 1.02; p 0.003] GCS (HR 0.88 [95% CI: 0.81, 0.96; p 0.003] and IVH (HR 2.23 [95% CI: 1.25, 3.98; p 0.007] were all associated with increased mortality. Percentage growth (cumulative OR 0.84 [95% CI: 0.75, 0.92; p 0.0001] initial ICH volume (cumulative OR 0.94 [95% CI: 0.91, 0.97; p 0.0001] GCS (cumulative OR 1.46 [95% CI: 1.21, 1.82; p 0.0001] and age (cumulative OR 0.95 [95% CI: 0.92, 0.98; p 0.0009] predicted outcome modified Rankin Scale. Gender, location, blood glucose, and blood pressure did not predict outcomes.  Hematoma growth is an independent determinant of both mortality and functional outcome after intracerebral hemorrhage. Attenuation of growth is an important therapeutic strategy.
Accelerated Training for Massive Classification via Dynamic Class Selection
Venue: AAAI
Year: 2018

Massive classification, a classification task defined over a vast number of classes (hundreds of thousands or even millions) has become an essential part of many real world systems, such as face recognition. Existing methods, including the deep networks that achieved remarkable success in recent years, were mostly devised for problems with a moderate number of classes. They would meet with substantial difficulties, e.g. excessive memory demand and computational cost, when applied to massive problems. We present a new method to tackle this problem. This method can efficiently and accurately identify a small number of "active classes" for each mini batch, based on a set of dynamic class hierarchies constructed on the fly. We also develop an adaptive allocation scheme thereon, which leads to a better tradeoff between performance and cost. On several large scale benchmarks, our method significantly reduces the training cost and memory demand, while maintaining competitive performance.
Contact angle measurement of natural materials.
Venue: Colloids and surfaces. B, Biointerfaces
Year: 2018

Contact angle (CA) is the most important parameter used to quantify the wettability of solid surfaces. In order to evaluate wettability performance, numerous methods have been developed to measure the CA of solid surfaces. Recent years have seen increased focus on the special wettability performance of various biological materials. Biomimetic wettability has become one of the most popular research fields, and novel CA measurements have been invented accordingly. In this protocol, we introduce several CA measurement techniques mainly based on the image capture method, which is commonly to investigate the wettability of natural materials. According to the solid/liquid/gas context, we classify CA measurements into three types: in air, under liquid, and air bubble measurements, and describe methods for each. The precise measurement of CA together with study of surface structure can reveal the mechanisms of special wettability, thus accelerating the investigation of biomaterials.
Energy Aware Analog Aggregation for Federated Learning with Redundant Data
Venue: ICC 2020 2020 IEEE International Conference on Communications (ICC)
Year: 2020

Federated learning (FL) enables workers to learn a model collaboratively by using their local data, with the help of a parameter server (PS) for global model aggregation. The high communication cost for periodic model updates and the nonindependent and identically distributed (i.i.d. data become major bottlenecks for FL. In this work, we consider analog aggregation to scale down the communication cost with respect to the number of workers, and introduce data redundancy to the system to deal with non i.i.d. data. We propose an online energy aware dynamic worker scheduling policy, which maximizes the average number of workers scheduled for gradient update at each iteration under a long term energy constraint, and analyze its performance based on Lyapunov optimization. Experiments using MNIST dataset show that, for non i.i.d. data, doubling data storage can improve the accuracy by 9.8% under a stringent energy budget, while the proposed policy can achieve close to optimal accuracy without violating the energy constraint.
Les determinants de la croissance economique en zone CEMAC
Year: 2017

Ce travail met en exergue a travers l'approche en donnees de panel les determinants de la croissance economique observee au cours de cette derniere decennie au sein de la Communaute Economique et Monetaire de l'Afrique Centrale (CEMAC) et necessaire pour son emergence economique. Pour ce faire, nous avons essentiellement utilise pour les estimations le logiciel stata 12, un echantillon des donnees de panel compose des six pays membres de la CEMAC allant de 2000 a 2013. Les resultats obtenus apres l'estimation des moindres carres ordinaires, le modele a effets fixes, le modele a effets aleatoires, la methode des moments generalises et les tests de specification montrent que le meilleur modele pour estimer ces types de donnees pour la CEMAC est le modele a effets fixes. Et que les principaux determinants de la croissance dans cette zone economique et monetaire sont les Investissements Directs Etrangers (IDE) et les credits accordes a l'economie (CREDIT) Si les IDE agissent positivement et d'une maniere tres significative sur la croissance economique, les CREDIT pour faute de bonne gouvernance agit significativement, mais d'une facon negative sur la croissance de la CEMAC.
Hidden Community Detection in Social Networks
Venue: Inf. Sci.
Year: 2018

Propose a new conception of hidden community for network analysis.Provide a meta approach called HICODE for finding the hidden communities.Several weakening methods are proposed to reduce the impact of the detected structure.The framework works iteratively to enhance the detection on both dominant communities and hidden communities.Extensive experiments demonstrate the effectiveness of the proposed method. This paper introduces a new graph theoretical concept of hidden community for analysing complex networks, which contain both stronger or dominant communities and weak communities. The weak communities are termed as being with the hidden community structure if most of its members also belong to the stronger communities. We propose a meta approach, namely HICODE (HIdden COmmunity DEtection) for identifying the hidden community structure as well as enhancing the detection of the dominant community structure. Extensive experiments on real world networks are carried out and the obtained results demonstrate that HICODE outperforms several state of the art community detection methods in terms of uncovering both the dominant and the hidden structure. Due to the difficulty of labeling all ground truth communities in real world datasets, HICODE provides a promising technique to pinpoint the existing latent communities and uncover communities for which there is no ground truth. Our finding in this work is significant to detect hidden communities in complex social networks.
THE EFFECT OF USING BRAINSTORMING ON THE SEVENTH GRADE STUDENTS' WRITING ACHIEVEMENT AT SMPN 8 JEMBER IN THE 2013/2014 ACADEMIC YEAR
Year: 2014

English is an important foreign language that needs to be mastered as it has been a global language that is used in almost all countries in the world. Therefore, in Indonesia students from junior high school up to university levels learn English. It is viewed as a compulsory subject taught by teacher in different levels. According to KTSP curriculum, the objective of teaching English is that students are expected to master the four language skills: speaking, reading, listening and writing, and mastering the language components namely grammar, pronunciation as well as vocabulary after finishing their studies. In the teaching learning process, those four language skills and language components are presented integratedly. Writing is very essential for students as Campbell and Rutherford,(1983:3) state that there are three reasons why writing is important. First, by writing, students try to use the grammatical structures, idioms, and vocabulary that they have learnt. Second, when students write, they also have a chance to be adventurous with the language. They have to be confident with their comprehension of language that is used in writing. Third, when students write, they necessarily become very involved with the new language. The effort to express the ideas is a way to reinforce learning. Students often discover something new to write or
Modeling wave effects on storm surge from different typhoon intensities and sizes in the South China Sea
Year: 2020

In this study, the dependence of typhoon induced storm surge and wave setup effect on typhoon intensity and size in the South China Sea are examined using the coupled ADCIRC SWAN model. The model is first verified based on in situ observations in the Pearl River Estuary during the passage of super typhoon Hato (2017) The model output is in good agreement with the observations, with relative errors and correlation coefficients of 4.97% and 0.80 (wind speed) 1.88% and 0.89 (storm surge) and 5.89% and 0.94 (significant wave height, SWH) The coupled model results show that the wave setup has a nonnegligible effect on storm surge simulation. Two sets of numerical experiments are designed to evaluate the variations in wave setup contribution to storm surges upon different typhoon intensities and sizes. With the increase of typhoon intensity or size, both maximum values and extended spatial ranges of the simulated SWH and storm surge increase significantly. In particular, the contribution of wave setup to the maximum storm surge increases nonlinearly with increasing typhoon intensity and size. For typhoon intensities ranging from 25 to 70 m s 1, the wave contribution to the storm surge increases from 0.02 to 0.17 m (1.96 6.66% For typhoons with sizes from 10 to 60 km, the wave contribution to storm surge increases from 0.05 to 0.13 m (4.89 6.53% However, when typhoon intensity is above 55 m s 1 or typhoon size is above 40 km, the percentage of wave setup contribution to the total storm surge tends to be saturated robustly. This study underlines the importance of incorporating wave setup effects into simulations of typhoon induced storm surges, which are very important in mitigating typhoon disasters and designing criteria related to societal infrastructure and coastal engineering.
Evaluating an approach to improving the adoption rate of wireless drug library updates for smart pumps.
Venue: American journal of health system pharmacy AJHP official journal of the American Society of Health System Pharmacists
Year: 2011

An academic medical center's approach to improving the adoption rate of wireless drug library updates for smart pumps was evaluated.  A multidisciplinary team composed of pharmacy, nursing, medical engineering, materials management, and patient equipment personnel at an academic medical center collaborated to update the drug libraries of more than 1800 smart pumps via a wireless control system. Two pilot tests were completed to identify and resolve issues before the live wireless update was attempted. The second pilot test, a passive approach, produced an adoption rate of 42% of 1804 pumps at the end of one week and a rate of 56% on day 10. The goal of 80% was not achieved until day 22. The change to an active multidisciplinary process three months later produced an adoption rate of 80% for 1869 pumps on day 10, resulting in a 45.4% increase in the adoption rate between the two trials on day 10 (p 0.001) Communication regarding the updates was disseminated via e mail to the entire organization, with fliers posted on all patient care units, and verbally during staff meetings. Patient equipment personnel manually tagged each pump with a blue zip tie after verifying the update to easily identify which pumps had been updated. Areas for improvement include increasing communication to the staff detailing when the update will occur and changing the day of the week the update is performed.  A multidisciplinary team actively engaged in the updating of wireless i.v. smart pump drug libraries reduced the amount of time required to reach a goal adoption rate of 80%
Development of REBCO HTS Power Cables
Year: 2012

A Japanese national project, called "Materials Power applications of Coated Conductors (M PACC)" started in FY2008. In this project, we are developing 66 kV/5 kA large current high temperature superconducting (HTS) cable and 275 kV/3 kA high voltage HTS cable using REBCO tapes. These HTS cables are expected as a compact size with large capacity and low loss power transmission. We have examined AC loss, thermal characteristics of the cables under over current, the optimum cable design and so on. After the design studies and elemental tests are completed, long cable systems will be built for verification purposes. This paper described the overview and current status of these HTS cables development in the M PACC project. (PACS: 84.71.Fk)
Evolving Trends in Surgical Management of Breast Cancer: An Analysis of 30 Years of Practice Changing Papers
Venue: Frontiers in Oncology
Year: 2021

The management of breast cancer has evolved into a multidisciplinary evidence based surgical speciality, with emphasis on conservative surgery. A number of landmark trials have established lumpectomy followed by radiation as the standard of care for many patients. The aim of this study is to construct a narrative review of recent developments in the surgical management of breast cancer and how such developments have impacted surgical practice. A comprehensive literature search of Pubmed was conducted. The latest search was performed on October 31st, 2020. Search terms "breast cancer" were used in combinations with specific key words and Boolean operators relating to surgical management. The reference lists of retrieved articles were comprehensively screened for additional eligible publications. Articles were selected and reviewed based on relevance. We selected publications in the past 10 years but did not exclude commonly referenced and highly regarded previous publications. Review articles and book chapters were also cited to provide reference on details not discussed in the academic literature. This article reviews the current evidence in surgical management of early stage breast cancer, discusses recent trends in surgical practice for therapeutic and prophylactic procedures and provides commentary on implications and factors associated with these trends.
Evaluating the impact of virtual exchange on initial teacher education: a European policy experiment
Year: 2019

Virtual exchange refers to education programmes in which constructive communication and interaction takes place between individuals or groups from different cultural backgrounds with the support of educators or facilitators. Evaluating and Upscaling Telecollaborative Teacher Education (EVALUATE, http:/www.evaluateproject.eu/ was a European policy experimentation financed by Erasmus+ which studied the impact of a telecollaborative model of virtual exchange on student teachers. Between 2017 2018, the project consortium trained teacher trainers and organised virtual exchanges which involved over 1,000 student teachers at initial teacher education institutions. This entailed students interacting and collaborating with partner classes from other countries as an integral part of one of their courses. The research team then analysed the learning gains from these exchanges using qualitative and quantitative research methodologies. They also worked with representatives from European ministries of education to understand how virtual exchange could be upscaled in teacher education across Europe. This publication presents the findings of the EVALUATE experimentation and its implications for the education of future teachers. The study found that engaging student teachers in structured online intercultural collaboration as part of their formal learning can contribute to the development of their digital pedagogical, intercultural, and foreign language competences. It can also lead to innovation and international learning in the education of future teachers.
The effects of virtual racial embodiment in a gaming app on reducing prejudice
Year: 2016

This research experimentally examined the effectiveness of the use of virtual racial embodiment in a digital gaming application for reducing bias against a non dominant group. The findings of this study advance theory and have practical implications for the design of immersive diversity initiatives. We tested avatar creation and the gaming app as a new modality for stimulating perspective taking. In addition, we explored how sex and race intersect in virtual embodiment and stereotyping. Results showed that creating and embodying a Black avatar produced more favorable beliefs about African American men, but not African American women, and greater support for "pro minority" policies in comparison to creating and playing a White avatar. Avatar liking was a significant moderator, but self presence had no effect.