nils-herrmann/clean_scirepeval huggingface dataset:
Seismic sparse spike deconvolution via Toeplitz sparse matrix factorization

ABSTRACTWe have developed a new sparse spike deconvolution (SSD) method based on Toeplitz sparse matrix factorization (TSMF) a bilinear decomposition of a matrix into the product of a Toeplitz matrix and a sparse matrix, to address the problems of lateral continuity, effects of noise, and wavelet estimation error in SSD. Assuming the convolution model, a constant source wavelet, and the sparse reflectivity, a seismic profile can be considered as a matrix that is the product of a Toeplitz wavelet matrix and a sparse reflectivity matrix. Thus, we have developed an algorithm of TSMF to simultaneously deconvolve the seismic matrix into a wavelet matrix and a reflectivity matrix by alternatively solving two inversion subproblems related to the Toeplitz wavelet matrix and sparse reflectivity matrix, respectively. Because the seismic wavelet is usually compact and smooth, the fused Lasso was used to constrain the elements in the Toeplitz wavelet matrix. Moreover, due to the limitations of computer memory, large.
Large geomagnetic field anomalies revealed in Bronze to Iron Age archeomagnetic data from Tel Megiddo and Tel Hazor, Israel

Geomagnetic field measurements from the past few centuries show heightened secular variation activity in the southern hemisphere associated with the south Atlantic anomaly (SAA) It is uncertain whether geomagnetic anomalies at a similar scale have existed in the past owing to limited coverage and uncertainties in the paleomagnetic database. Here we provide new evidence from archaeological sources in the Levant suggesting a large positive northern hemisphere anomaly, similar in magnitude to the SAA during the 9th 8th centuries BCE, called "Levantine Iron Age anomaly" We also report an additional geomagnetic spike in the 8th century. The new dataset comprises 73 high precision paleointensity estimates from ca. 3000 BCE to 732 BCE, and five directional measurements between the 14th and the 9th centuries BCE. Well dated pottery and cooking ovens were collected from twenty archaeological strata in two large contemporaneous stratigraphical mounds (tells) in  Tel Megiddo and Tel Hazor. The new data are combined with previously published data and interpreted automatically using the PmagPy Thellier GUI program. The Tel Megiddo and Tel Hazor data sets demonstrate excellent internal consistency and remarkable agreement with published data from Mesopotamia (Syria) The data illustrate the evolution of an extreme geomagnetic high that culminated in at least two spikes between the 11th and the 8th centuries BCE (Iron Age in the Levant) The paleomagnetic directional data of the 9th century BCE show positive inclination anomalies, and deviations of up to 22deg from the averaged geocentric axial dipole (GAD) direction. From comparison of the Levantine archaeomagnetic data with IGRF model for 2015 we infer the "Levantine Iron Age anomaly" between the 10th and the 8th centuries BCE is a local positive anomaly. The eastward extent of the anomaly is currently unknown.
Which Algorithms Suit Which Learning Environments? A Comparative Study of Recommender Systems in TEL
Venue: EC TEL

In recent years, a number of recommendation algorithms have been proposed to help learners find suitable learning resources on line. Next to user centered evaluations, offline datasets have been used to investigate new recommendation algorithms or variations of collaborative filtering approaches. However, a more extensive study comparing a variety of recommendation strategies on multiple TEL datasets is missing. In this work, we contribute with a data driven study of recommendation strategies in TEL to shed light on their suitability for TEL datasets. To that end, we evaluate six state of the art recommendation algorithms for tag and resource recommendations on six empirical datasets: a dataset from European Schoolnets TravelWell, a dataset from the MACE portal, which features access to meta data enriched learning resources from the field of architecture, two datasets from the social bookmarking systems BibSonomy and CiteULike, a MOOC dataset from the KDD challenge 2015, and Aposdle, a small scale workplace learning dataset. We highlight strengths and shortcomings of the discussed recommendation algorithms and their applicability to the TEL datasets. Our results demonstrate that the performance of the algorithms strongly depends on the properties and characteristics of the particular dataset. However, we also find a strong correlation between the average number of users per resource and the algorithm performance. A tag recommender evaluation experiment reveals that a hybrid combination of a cognitive inspired and a popularity based approach consistently performs best on all TEL datasets we utilized in our study.
The impact of customer attractiveness and supplier satisfaction on becoming a preferred customer

This paper discusses how a firm can become preferred customer, defined as a particular buying firm to whom the supplier allocates better resources than less preferred buyers. Two concepts play a central role for a firm aiming to become preferred customer: (i) customer attractiveness and (ii) supplier satisfaction. However, the current literature still lacks a clear discussion on the conceptual differences between these constructs and their attributes and is ambiguous with regard to the relationships between the concepts. This study addresses these shortcomings. We examine customer attractiveness and supplier satisfaction as distinct conceptual variables and test how these constructs relate to each other and to preferred customer status. We build upon practitioner input and survey data from 91 suppliers to do so. Our analyses show that the impact of customer attractiveness on preferential resource allocation from suppliers is significantly mediated by supplier satisfaction. These findings expand the current understanding of these concepts. In addition, our findings might help managers better evaluate their relationships with suppliers and align their strategies accordingly to obtain better resources from their suppliers
ELM 2016 data update and new functionality of the eukaryotic linear motif resource
Venue: Nucleic Acids Res.

The Eukaryotic Linear Motif (ELM) resource (http:/elm.eu.org) is a manually curated database of short linear motifs (SLiMs) In this update, we present the latest additions to this resource, along with more improvements to the web interface. ELM 2016 contains more than 240 different motif classes with over 2700 experimentally validated instances, manually curated from more than 2400 scientific publications. In addition, more data have been made available as individually searchable pages and are downloadable in various formats.
RegulonDB version 9.0: high level integration of gene regulation, coexpression, motif clustering and beyond
Venue: Nucleic Acids Res.

RegulonDB (http:/regulondb.ccg.unam.mx) is one of the most useful and important resources on bacterial gene regulation,as it integrates the scattered scientific knowledge of the best characterized organism, Escherichia coli K 12, in a database that organizes large amounts of data. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Here, we summarize our progress with RegulonDB since our last Nucleic Acids Research publication describing RegulonDB, in 2013. In addition to maintaining curation up to date, we report a collection of 232 interactions with small RNAs affecting 192 genes, and the complete repertoire of 189 Elementary Genetic Sensory Response units (GENSOR units) integrating the signal, regulatory interactions, and metabolic pathways they govern. These additions represent major progress to a higher level of understanding of regulated processes. We have updated the computationally predicted transcription factors, which total 304 (184 with experimental evidence and 120 from computational predictions) we updated our position weight matrices and have included tools for clustering them in evolutionary families. We describe our semiautomatic strategy to accelerate curation, including datasets from high throughput experiments, a novel coexpression distance to search for 'neighborhood' genes to known operons and regulons, and computational developments.
High throughput discovery of novel developmental phenotypes
Venue: Nature

Approximately one third of all mammalian genes are essential for life. Phenotypes resulting from knockouts of these genes in mice have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5,000 knockout mouse lines, here we identify 410 lethal genes during the production of the first 1,751 unique gene knockouts. Using a standardized phenotyping platform that incorporates high resolution 3D imaging, we identify phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes, thus providing a dataset that facilitates the prioritization and validation of mutations identified in clinical sequencing efforts.
Electrochemical Characterization of a New Biodegradable FeMnSi Alloy Coated with Hydroxyapatite Zirconia by PLD Technique

Biodegradable alloys are very attractive biomaterials. Electrochemical impedance spectroscopy (EIS) and linear potentiodynamic polarization (LPP) techniques were used for the study of the electrochemical behavior of uncoated FeMnSi and coated FeMnSi with hydroxyapatite
A machine learning approach for student assessment in E learning using Quinlan's C4.5, Naive Bayes and Random Forest algorithms
Venue: 2016 19th International Multi Topic Conference (INMIC)

Student Assessment on e learning platforms is a debated subject. The focal emphasis of this research study is to predict fair/transparent student evaluation using machine learning algorithms. A prediction on students' final grade showing whether the student will pass or fail would benefit the student/instructor and act as a guide for future recommendations/evaluations on performance. An in depth study on the assessment techniques for e learning such as Markov Model, metacognitive perspectives has been conducted. A proposed model for fair/transparent student evaluation/performance has also been presented. Specific parameters have been defined that are then efficaciously tested by applying machine learning algorithms. In this study, classifiers such as Decision Trees J48, Naive Bayes and Random Forest are used to progress the excellence of student data by initially eradicating noisy data, and consequently getting better prognostic accuracy. The scope of the paper has been set for undergraduate programs. The experimental results endow with set of guidelines to those students who have low grades. Performance testing has also been conducted for verification, accuracy and validity of results.
Ecosystem services provided by urban gardens in Barcelona, Spain: Insights for policy and planning

In many European cities, urban gardens are seen as increasingly important components of urban green space networks. We adopt an ecosystem services framework to assess contributions of urban gardens to the quality of of their users. First, we identify and characterize ecosystem services provided by urban gardens. Secondly, we assess the demographic and socioeconomic profile of its beneficiaries and the relative importance they attribute to different ecosystem services. Next we discuss the relevance of our results in relation to critical policy challenges, such as the promotion of societal cohesion and healthy lifestyles. Data were collected through 44 semi structured interviews and a survey among 201 users of 27 urban gardens in Barcelona, Spain, as well as from consultation meetings with local planners. We identified 20 ecosystem services, ranging from food production over pollination to social cohesion and environmental learning. Among them, cultural ecosystem services (non material benefits people derive from their interaction with nature) stand out as the most widely perceived and as the most highly valued. The main beneficiaries of ecosystem services from urban gardens are elder, low middle income, and migrant people. Our results about the societal importance of urban gardens were deemed highly relevant by the interviewed green space planners in Barcelona, who noted that our data can provide basis to support or expand existing gardening programs in the city. Our research further suggests that ecosystem services from urban gardens can play an important role in addressing several urban policy challenges in cities, such as promoting stewardship of urban ecosystems, providing opportunities for recreation and healthy lifestyles, and promoting social cohesion. We conclude that urban gardens and associated ecosystem services can play an important in urban policies aimed at enhancing quality of life in cities, particularly if access to their benefits is expanded to larger segments of the population.
De l'apprentissage procedural de la programmation a l'integration interdisciplinaire de la programmation creative

L'introduction de l'apprentissage de la programmation a l'ecole est une tendance croissante dans les ecoles primaires a l'echelle internationale. La programmation est consideree comme une litteratie du 21e siecle qui permet de developper les strategies de pensee informatique comme l'organisation logique, l'abstraction et la resolution de problemes. Dans cette chronique, nous presentons l'etat actuel des initiatives d'integration de la programmation a l'ecole et, ensuite, nous soulignons les differences entre, d'un cote, l'apprentissage de la programmation de maniere procedurale et decontextualisee du curriculum et, de l'autre cote, la programmation creative comme outil de modelisation de connaissances permettant un arrimage interdisciplinaire au curriculum.
PREPARATION AND EVALUATION OF NATURAL COLOR EXTRACT FOR AYURVEDIC SYRUP

The plant Butea monosperma is very popular among the peoples of Northern Ghana, where the leaves are used in soups; and calyces for soft drinks and also used medicinally. It has been found to possess several health benefits. The flower has a very rich orange colour, which this study aims as investigating its suitability as a coloring agent in pharmaceutical syrup.  The plant parts (flowers) are used in experimental work for investigating the suitability of Butea monosperma extract as coloring agent for pediatric syrup.1kg of dried Butea Monosperma flowers were weighed out and spread out in a thin layer. After authentication, sample of Butea monosperma was washed for ten seconds in water (to avoid losing colour) Excess water was blotted from the sample with a clean towel, and dried in a hot air oven for four hours at 300oC before the extraction procedure. Then the solvent for extraction (water, drug solvent ratio taken 1:8) was filled in Soxhlet apparatus. After completion of the extraction procedure, the extract was taken out and evaporated up to semisolid consistency and then amount in percentages was determined in w/w.  The colour extract from Butea monosperma is found suitable as a pharmaceutical coloring agent. Tartrazine can be replaced with this natural source of colour, promoting the health of our people.
Unmanned Aerial Vehicle Emergency Landing Site Identification System Using Machine Vision

Unmanned Aerial Vehicles (UAVs) rely on navigation commands from autonomous flight control system or from Ground Control System (GCS) via line ofsight wireless data link. UAV needs to perform immediate landing on predefined airfield in case of extreme emergency like navigation, data link, engine or control surface failure, that cannot be accomplished in some cases and accidents can occur which can result collateral damage as well. This paper presents the system design which can discover the appropriate area within the surroundings for immediate landing in case of emergency. The proposed system design consists of two stages. During first stage, system takes top view images from UAV onboard camera, then image processing algorithm extracts and refine the attributes of the image. In second stage, machine learning based algorithm evaluates the results from previous stage, and based on its previous training, decides whether the area visible in image is good for safe landing or not. We implement proposed system design in MATLAB and the approach used is validated with experimental results on test data. Proposed system design uses combination of simple techniques, which makes it less computationally intensive, having reduced latency, low implementation cost and easy to implement on high speed real time hardware like FPGA/ASIC.
A comparison of action selection methods for implicit policy method reinforcement learning in continuous action space
Venue: 2016 International Joint Conference on Neural Networks (IJCNN)

In this paper I investigate methods of applying reinforcement learning to continuous state and action space problems without a policy function. I compare the performance of four methods, one of which is the discretisation of the action space, and the other three are optimisation techniques applied to finding the greedy action without discretisation. The optimisation methods I apply are gradient descent, Nelder Mead and Newton's Method. The action selection methods are applied in conjunction with the SARSA algorithm, with a multilayer perceptron utilized for the approximation of the value function. The approaches are applied to two simulated continuous state and action space control problems: Cart Pole and double Cart Pole. The results are compared both in terms of action selection time and the number of trials required to train on the benchmark problems.
Cell Suspension Culture and Secondary Metabolite Production of Curculigo Orchioides Gaertn An Endangered Medicinal Plant

Curculigo orchioides Gaertn. cell suspension culture was developed in liquid Murashige and Skoog (MS) medium significantly without any growth supplements. The secondary metabolite analysis was done in cells and their respective media at the time of each harvest. Secondary metabolite analysis was also done of the in vitro and in vivo roots of C. orchioides. All extractions were made in RO water and methanol. Concentrations of phenolics and flavonoids in the in vivo roots were found to be the highest followed by decreasing trend in in vitro and 5, 10 and 15 days old suspension cells. When analysis is done to estimate total production possibilities, it turns out that production of the phenolics and flavonoids are highest in suspension cells and least in the in vitro and in vivo roots. All estimations were done in the fresh samples. Analytical data of secondary metabolites of C. orchioides samples were compared with the Viagra salts Sildenafil citrate and Tadalafil. It was observed to have the similar salts/perspective compound (if not identical) in all the Curculigo samples studied. Since C. orchioides known to be use as aphrodisiac, analytical studies of the extracts mentioned above was done using viagra salts Sildenafil citrate and Tadalafil as standards. Our data shows positive conversions. Hence, processing and purification of these compounds with aphrodisiac properties from C. orchioides suspension cells should be possible and have economic feasibility.
Textual Sources For The Study Of Zoroastrianism

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Computational Intelligence and Financial Markets: A Survey and Future Directions
Venue: Expert Syst. Appl.

We propose a survey of soft computing techniques applied to financial market.We surveyed several primary studies proposed in the literature.A framework for building an intelligent trading system was proposed.Future directions of this research field are discussed. Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field.
Integration Analysis of Conceptual Design and Stealth Aerodynamic Characteristics of Combat Aircraft

In order to study stealth strike fighter, an analysis on stealth aerodynamic integration and conceptual design is conducted. A conceptual 3 D digital model with internal antiship missiles and air to air missiles is designed in CATIA software. Based on the physical optics and equivalent electromagnetic current methods, using the self programmed RCSAnsys software, the Radar Cross Section (RCS) characteristics and characteristics of scattering intensity distribution of the model are numerically simulated. Based on the turbulence theory of standard k e equations, using Fluent software, the pressure, velocity and lift to drag characteristics of the conceptual aircraft are numerically simulated. The simulation results show that the stealth and aerodynamic characteristics of the conceptual aircraft can be designed through integration analysis process, which can provide technical support to the design of the advanced operational aircrafts.
Fifteen new species of Penicillium
Venue: Persoonia

We introduce 15 new species of Penicillium isolated from a diverse range of locations, including Canada, Costa Rica, Germany, Italy, New Zealand, Tanzania, USA and the Dry Valleys of Antarctica, from a variety of habitats, including leaf surfaces in tropical rain forests, soil eaten by chimpanzees, infrabuccal pockets of carpenter ants, intestinal contents of caterpillars and soil. The new species are classified in sections Aspergilloides (1) Canescentia (2) Charlesia (1) Exilicaulis (3) Lanata Divaricata (7) and Stolkia (1) Each is characterised and described using classical morphology, LC MS based extrolite analyses and multigene phylogenies based on ITS, BenA and CaM. Significant extrolites detected include andrastin, pulvilloric acid, penitrem A and citrinin amongst many others.