nils-herrmann/clean_scirepeval huggingface dataset:
Isolation of Phosphate Solubilizing Bacteria from White Lupin (Lupinus albus L. Rhizosphere Soils Collected from Gojam, Ethiopia

Phosphorus is an essential macronutrient. A great portion of phosphorus from chemical fertilizers becomes insoluble and unavailable to plants because of its conversion into salts. Phosphate solubilizing microorganisms as a phosphorus biofertilizer improve soil fertility by solubilizing insoluble phosphate salts and increase crop production. This research aimed in isolation and characterization of phosphate solubilizing bacteria. Sixtythree soil samples collected from seven different parts of white lupin growing areas of Gojam, Ethiopia. Halo zone formation and plate screening method used for isolation. Based on halo zone formation; 152 phosphate solubilizing bacterial isolates obtained. Based on their solubilization index four isolates selected for subsequent experiments and characterization. The four selected isolates tested for their solubilization efficiency on solid media. Isolates HUPSB 35 and HUPSB 45 appeared maximum solubilization index (4.5 mm) Isolate HUPSB 57 was unique with its colony formation; salt tolerance (able to grow up to 10% and utilization of wide range of carbon sources (utilize all the tested sugars) Isolate HUPSB 27 showed a wide range of pH and temperature preference (100% growth at 10, 15, 20, 25 and 30degC in all tested pH)
A Price Index for Canada, 1688 to 1850

I construct the first price index for any region of Canada that spans from the colonial era to the mid nineteenth century. I use prices collected from the account books of religious congregations with estates throughout the modern day province of Quebec. The consistency of the type of price quotations in the source material, the high frequency of observations for many goods, the vast number of goods and the inclusion of numerous non agricultural and non food goods represent a substantial improvement over previous indexes. Price trends are mildly different from those of existing, but less comprehensive, price indexes. This new index is used to link up with indexes post 1850 in order to create a 328 years long price index for Canada.
Concert Lighting Techniques Art And Business

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Micro scale H2 CO2 Dynamics in a Hydrogenotrophic Methanogenic Membrane Reactor
Venue: Front. Microbiol.

Biogas production is a key factor in a sustainable energy supply. It is possible to get biogas with very high methane content if the biogas reactors are supplied with exogenous hydrogen, and one of the technologies for supplying hydrogen is through gas permeable membranes. In this study the activity and stratification of hydrogen consumption above such a membrane was investigated by use of microsensors for hydrogen and pH. A hydrogenotrophic methanogenic community that was able to consume the hydrogen flux within 0.5 mm of the membrane with specific rates of up to 30 m3 H2 m 3 day 1 developed within 3 days in fresh manure and was already established at time zero when analyzing slurry from a biogas plant. The hydrogen consumption was dependent on a simultaneous carbon dioxide supply and was inhibited when carbon dioxide depletion elevated the pH to 9.2. The activity was only partially restored when the carbon dioxide supply was resumed. Bioreactors supplied with hydrogen gas should thus be carefully monitored and either have the hydrogen supply disrupted or be supplemented with carbon dioxide when the pH rises to values about 9.
On the scale up of micro reactors for liquid liquid reactions

The scale up of a micro reactor with a mixer designed for liquid liquid reactions is investigated using a 3/7th approach that scales the hydraulic diameter at increased flow rates and keeps constant the average rate of energy dissipation. Smaller d h 283 um) and larger scale d h 714 um) mixers are compared using single phase pressure drop measurements and a liquid liquid reactive extraction. The single phase tests demonstrate that the energy dissipation rate of the larger scale mixer is comparable to the smaller scale mixer at flow rates ~8.5 times greater. The overall volumetric mass transfer coefficients of the reactive extraction, K c a, of both scale mixers were also similar at equal energy dissipation rate in the drop flow regime. Finally, the upper limit of the sizing approach is discussed and compared with commercially available static mixers.
Selective laser melting of titanium alloy with 50 wt% tantalum: Microstructure and mechanical properties

In this study, selective laser melting (SLM) was used to fabricate samples of titanium tantalum (TiTa) alloy comprising 50 wt% of each element. Based on observation from scanning electron microscopy, as fabricated samples comprised of randomly dispersed pure tantalum particles in a TiTa matrix. The microstructure exhibited equiaxed grains of b titanium and tantalum in random orientations, determined by combination of field emission scanning electron microscopy, electron back scatter diffraction and X ray diffraction. The resulting samples have ultimate tensile strength of 924.64 9.06 MPa and elastic modulus of 75.77 4.04 GPa. The TiTa alloy produced can be a potential material for biomedical applications due to its high strength to modulus ratio, as compared to Ti6Al4V and commercially pure titanium.
Pollutant emissions during the pyrolysis and combustion of flexible polyurethane foam.
Venue: Waste management

Thermal decomposition of flexible polyurethane foam (FPUF) was studied under nitrogen and air atmospheres at 550degC and 850degC using a laboratory scale reactor to analyse the evolved products. Ammonia, hydrogen cyanide and nitrile compounds were obtained in high yields in pyrolysis at the lower temperature, whereas at 850degC polycyclic aromatic hydrocarbons (PAHs) and other semivolatile compounds, especially compounds containing nitrogen (benzonitrile, aniline, quinolone and indene) were the most abundant products. Different behaviour was observed in the evolution of polychlorodibenzo p dioxins and furans (PCDD/Fs) at 550degC and 850degC. At 550degC, the less chlorinated congeners, mainly PCDF, were more abundant. Contrarily, at 850degC the most chlorinated PCDD were dominant. In addition, the total yields of PCDD/Fs in the pyrolysis and combustion runs at 850degC were low and quite similar.
Utilizing an ensemble of SVMs with GMM voting based mechanism in predicting dangerous seismic events in active coal mines
Venue: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS)

This paper presents an application of a Gaussian Mixture Model based voting mechanism for an ensemble of Support Vector Machines (SVMs) to the problem of predicting dangerous seismic events in active coal mines. The author proposes a method of preparing an ensemble of SVMs with different parameters and using the "wisdom of the crowd" for a classification problem. Experiments performed during the research showed an improvement in the quality of the classification after the mixture of Gaussian distributions was applied as votes distribution. The author also proposes a method of data selection for long sequences of measurement arranged chronologically with highly unbalanced occurrence of the positive class in the two class classification problem. Finally, using the proposed model to solve the problem defined by the organizers of AAIA'16 DM showed an increase in the stability of the ensemble classifier and an improvement in the quality of the classification problem solution.
Face occlusion detection using skin color ratio and LBP features for intelligent video surveillance systems
Venue: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS)

A face occlusion detection scheme which is based on both skin color ratio (SCR) and Local Binary Pattern (LBP) feature, is proposed. The proposed method mainly consists of four steps: foreground extraction, head detection, feature extraction, and occlusion detection. First, foreground is extracted by codebook background subtraction algorithm. Then, the head region is located using HOG head detector. After that, the skin color ratio and LBP feature are extracted. Finally, SVM is trained based on LBP feature. The recognition result of SVM and the result of skin color ratio feature are merged by weighted voting strategy, and then occluded faces are classified as three categories: concealed, partially concealed, and visible. Experimental results show that the proposed detection system can achieve desirable results in intelligent video surveillance systems.
Random subspace ensemble artificial neural networks for first episode schizophrenia classification
Venue: 2016 Federated Conference on Computer Science and Information Systems (FedCSIS)

Computer aided schizophrenia diagnosis is a difficult task that has been developing for last decades. Since traditional classifiers have not reached sufficient sensitivity and specificity, another possible way is combining the classifiers in ensembles. In this paper, we take advantage of random subspace ensemble method and combine it with multi layer perceptron (MLP) and support vector machines (SVM) Our experiment employs voxel based morphometry to extract the grey matter densities from 52 images of first episode schizophrenia patients and 52 healthy controls. MLP and SVM are adapted on random feature vectors taken from predefined feature pool and the classification results are based on their voting. Random feature ensemble method improved prediction of schizophrenia when short input feature vector (100 features) was used, however the performance was comparable with single classifiers based on bigger input feature vector (1000 and 10000 features)
Using sensor and spectral analysis to classify botanical origin and determine adulteration of raw honey

The feasibility of sensors (Electronic Nose, EN; Electronic Tongue, ET) and spectra (Near Infrared spectrum, NIR; Mid Infrared spectrum, MIR) to evaluate raw honey samples (Vitex, Jujube and Acacia) was explored. Partial least squares discriminant analysis (PLSDA) model, support vector machine (SVM) algorithms model and Interval partial least squares (iPLS) model were used to classify the botanical origin. The results indicate that spectra and sensors could classify the botanical origin of honey rapidly and accurately, since total accuracy for calibration and prediction sets was all almost 100% in EN and ET analysis by SVM model and in NIR and MIR analysis by iPLS model. Then principal components analysis (PCA) and PLSDA model were used to determine the adulterants. Total accuracy for calibration and prediction sets was all above 96% in NIR, MIR and ET by PLSDA model. The results indicate that ET is more suitable for detecting honey adulteration.
A new integrated GIS based analysis to detect hotspots: A case study of the city of Sherbrooke.
Venue: Accident; analysis and prevention

This paper proposes a two step integrated method for identifying traffic accident (TA) hotspots on a roadway network. The first step includes a spatial analysis method called network kernel density estimation (KDE) The second step is a network screening method using the critical crash rate, which it described in the Highway Safety Manual (HSM) The method was examined by using three years of TAs (2011 2013) in Sherbrooke, Canada. The network KDE uses TAs to graphically display sites with a high crash density. Two different crash patterns were used for identifying these locations: (1) a crash pattern that includes three year aggregated crash data, and (2) a crash pattern that involves three year merged crash data. The results of the two crash patterns were evaluated based on a prediction accuracy index (PAI) It was found that the results obtained from the merged crash data outperformed the other. On the other hand, crash clustering in a site does not imply a site is hotspot and it is better to tested by other factors. High crash density locations were then tested by the critical crash rate, which helps to create an accurate comparison of sites. The importance of the critical crash rate is that it takes several factors into account such as the amount of exposure, the type of intersection, variance in crash data, etc. We realized that the hotspots determined using the two methods reflect very problematic locations and filter out the locations that do not have a problem. This approach could help transportation authorities and safety specialists to identify and prioritize sites that require more safety attention.
Analyse d'un outil d'evaluation en mathematiques entre une logique de competences et une logique de contenu

La Federation Wallonie Bruxelles met a la disposition des enseignants des outils d'evaluation susceptibles de diagnostiquer la capacite des eleves a mobiliser et a combiner, en situation inedite, un ensemble de procedures apprises. Si plusieurs chercheurs se sont investis dans la reflexion scientifique autour de ces outils et de leur pouvoir diagnostique, la question de l'impact des contenus specifiques evalues par ceux ci est peu envisagee. En analysant un outil centre sur l'algebre elementaire, cet article a pour ambition de rendre compte de la plus value apportee par un regard alimente par les recherches menees dans la discipline ciblee. Depassant les constats generaux poses par la passation de l'outil telle que prevue par les auteurs dans une logique de competences, les resultats mettent en perspective les demarches mises en oeuvre par les eleves devant les differentes taches proposees. Au dela des constats specifiques a la tache analysee, cet article montre l'importance de regards croises sur la question de l'evaluation des competences.
Markov Chains And Mixing Times

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Learning to segment with image level annotations
Venue: Pattern Recognit.

Recently, deep convolutional neural networks (DCNNs) have significantly promoted the development of semantic image segmentation. However, previous works on learning the segmentation network often rely on a large number of ground truths with pixel level annotations, which usually require considerable human effort. In this paper, we explore a more challenging problem by learning to segment under image level annotations. Specifically, our framework consists of two components. First, reliable hypotheses based localization maps are generated by incorporating the hypotheses aware classification and cross image contextual refinement. Second, the segmentation network can be trained in a supervised manner by these generated localization maps. We explore two network training strategies for achieving good segmentation performance. For the first strategy, a novel multi label cross entropy loss is proposed to train the network by directly using multiple localization maps for all classes, where each pixel contributes to each class with different weights. For the second strategy, the rough segmentation mask can be inferred from the localization maps, and then the network is optimized based on the single label cross entropy loss with the produced masks. We evaluate our methods on the PASCAL VOC 2012 segmentation benchmark. Extensive experimental results demonstrate the effectiveness of the proposed methods compared with the state of the arts. HighlightsLocalization map generation is proposed by using the hypothesis based classification.A novel multi label loss is proposed to train the network based on localization maps.An effective method is proposed to predict the rough mask of the given training image.Our methods achieve new state of the art results on PASCAL VOC 2012 benchmark.
Translation to Portuguese and cultural adaptation of Food Allergy Quality of Life Questionnaire Parent Form (FAQLQ PF)
Venue: European annals of allergy and clinical immunology

Food allergy is a health problem with significant negative impact in Quality of Life (QoL) We aimed to translate into Portuguese and culturally adapt to our population the Food Allergy Quality of Life Questionnaire Parent Form (FAQLQ PF) Cross cultural translation was performed according to guidelines. Linguistic validation consisted in 3 steps: forward translation, backward translation and comprehensibility testing. A consensual version was obtained and tested in parents of food allergic children by cognitive debriefing. Twelve questionnaires were fulfilled, all completed in 15 min. No comments, doubts or suggestions were posed, except for 2 parents regarding a question about the number of food their children had to avoid. Two gave special positive feedback about the utility of FAQLQ PF. Changes have been included after this pre test in accordance to doubts and suggestions of participants, and the Portuguese version is now able to be used in clinics and research.
A Study on The English Translation of Special Dishes Along Taihang Mountainous Areas In Hebei Province

In February of 2016, China released the first list of creating the national global tourism demonstration area, and eleven places in Hebei province were on the list. For those in the tourism demonstration zone list, the central and local government will give financial support and give priority to arrange tourism diplomacy, which brings new challenges to the catering industry in Taihang mountainous areas. In Taihang Mountain areas, there are a lot of beautiful tourist attractions with a rich variety of organic diet. The food industry plays an important role in promoting the development of international tourism and accelerating the international friends'understanding of china's rural culture. This paper investigates the current situation of rural development in Taihang mountainous areas and the translation problems in the rural home inns and it aims to explore the factors which influence the diet translation. Finally, the autor proposes some translation stratigies.
Information content measures of semantic similarity between documents based on Hadoop system
Venue: 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM)

Retrieving documents in response to the user's query is the most commonly text retrieval task. For our work, we have mainly focused on detecting the semantic similarity between documents in large documents collection and queries. In this paper, we investigated MapReduce as a specific framework for managing distributed processing in dataset pattern and semantic similarity measures of documents. Then we study the state of the art of different approaches for computing the semantic similarity of documents. We propose an approach based on parallel algorithm of semantic similarity measures using MapReduce and WordNet to detect the relevant documents in the face of the query. Finally, we are leading basic experiments to assess the performance of the proposed approach and noted the leverage of Hadoop and MapReduce to the semantic similarity measures between documents.
The Development of Bimodal Bilingualism: Implications for Linguistic Theory.
Venue: Linguistic approaches to bilingualism

A wide range of linguistic phenomena contribute to our understanding of the architecture of the human linguistic system. In this paper we present a proposal dubbed Language Synthesis to capture bilingual phenomena including code switching and 'transfer' as automatic consequences of the addition of a second language, using basic concepts of Minimalism and Distributed Morphology. Bimodal bilinguals, who use a sign language and a spoken language, provide a new type of evidence regarding possible bilingual phenomena, namely code blending, the simultaneous production of (aspects of) a message in both speech and sign. We argue that code blending also follows naturally once a second articulatory interface is added to the model. Several different types of code blending are discussed in connection to the predictions of the Synthesis model. Our primary data come from children developing as bimodal bilinguals, but our proposal is intended to capture a wide range of bilingual effects across any language pair.
Goals Of Linguistic Theory

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Deep multi scale video prediction beyond mean square error
Venue: ICLR

Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some degree, its content and dynamics. This is why pixel space video prediction may be viewed as a promising avenue for unsupervised feature learning. In addition, while optical flow has been a very studied problem in computer vision for a long time, future frame prediction is rarely approached. Still, many vision applications could benefit from the knowledge of the next frames of videos, that does not require the complexity of tracking every pixel trajectories. In this work, we train a convolutional network to generate future frames given an input sequence. To deal with the inherently blurry predictions obtained from the standard Mean Squared Error (MSE) loss function, we propose three different and complementary feature learning strategies: a multi scale architecture, an adversarial training method, and an image gradient difference loss function. We compare our predictions to different published results based on recurrent neural networks on the UCF101 dataset