nils-herrmann/clean_scirepeval huggingface dataset:
Geostationary Satellite Station Keeping Using Convex Optimization

This paper introduces a novel affine formulation of the equations of motion of a satellite in a geostationary orbit and, based on this formulation, presents a method for determining station keeping maneuvers using convex optimization. The state equations are written as a linear, time varying system and include all dominant perturbing accelerations. An investigation into the modeling errors is presented to validate and establish fidelity in the approach. The linearity of the system enables the formulation of the station keeping problem as a convex optimization problem. The optimization based formulation allows to explicitly account for constraints on state and control variables. The resulting problem can be solved reliably, requiring only limited computational resources, making the method not only applicable to on ground open loop maneuver planning (the current state of the art) but also to a closed loop onboard implementation. The method is generic and can be applied to satellites equipped with high thrus.
Key Role of Sequencing to Trace Hepatitis A Viruses Circulating in Italy During a Large Multi Country European Foodborne Outbreak in 2013
Venue: PloS one

Hepatitis A Virus (HAV) outbreaks are being recognized as an emerging public health problem in industrialized countries. In 2013 three foodborne HAV outbreaks occurred in Europe and one in USA. During the largest of the three European outbreaks, most cases occurred in Italy >1,200 cases as of March 31, 2014) A national Task Force was established at the beginning of the outbreak by the Ministry of Health. Mixed frozen berries were early demonstrated to be the source of infection by the identity of viral sequences in patients and in food. In the present study the molecular characterization of HAV isolates from 355 Italian cases is reported.  Molecular characterization was carried out by PCR/sequencing (VP1/2A region) comparison with reference strains and phylogenetic analysis.  A unique strain was responsible for most characterized cases (235/355, 66.1% Molecular data had a key role in tracing this outbreak, allowing 110 out of the 235 outbreak cases (46.8% to be recognized in absence of any other link. The data also showed background circulation of further unrelated strains, both autochthonous and travel related, whose sequence comparison highlighted minor outbreaks and small clusters, most of them unrecognized on the basis of epidemiological data. Phylogenetic analysis showed most isolates from travel related cases clustering with reference strains originating from the same geographical area of travel.  In conclusion, the study documents, in a real outbreak context, the crucial role of molecular analysis in investigating an old but re emerging pathogen. Improving the molecular knowledge of HAV strains, both autochthonous and circulating in countries from which potentially contaminated foods are imported, will become increasingly important to control outbreaks by supporting trace back activities, aiming to identify the geographical source(s) of contaminated food, as well as public health interventions.
The relationship between observed and perceived assessments of the coach created motivational environment and links to athlete motivation

The majority of research examining the relationship between the coach created motivational and athlete motivation has relied on self report measures. Grounded in Duda's (2013) theoretically integrated model, the present study examined: (1) athletes' coaches' and observers' reports of the multidimensional motivational coaching environment in four European countries, (2) the interrelationships of these different perspectives of the motivational environment, and (3) links between the multidimensional environment and athletes' autonomous, controlled and amotivation.  We employed a cross sectional study design and utilized mixed methods to tap the variables of interest. Both descriptive and more sophisticated multi level statistical analyses were employed to test our hypotheses.  Seventy four grassroots soccer coaches and 882 youth athletes from England, France, Greece and Spain were recruited. Coaches were video recorded during a training session and observers rated the degree to which the coaching climate was autonomy supportive, controlling, task involving, ego involving and relatedness supportive. Athletes and coaches completed questionnaires assessing their perceptions of the coach created climate in relation to the aforementioned dimensions of the environment. Athletes also completed measure of their sport based motivation regulations.  A profile of the motivational environment and athlete motivation was presented actoss four countries. There were weak associations found between different perspectives of the multidimensional coaching environment. However, athletes' coaches' and observers' reports of features of the motivational environment emerged as significant predictors of athletes' autonomous, controlled and amotivation.  Results provide partial support for findings of previous studies examining athlete motivation correlates of the motivational environment relying solely on self report measures. Findings also point to the value of adopting a mixed methodological approach and including athletes' coaches' and observers' reports of the environment when time and resources allow.
High school athletes' self determined motivation: The independent and interactive effects of coach, father, and mother autonomy support

The purpose of the study was to examine the independent and interactive influences of athletes' perceptions of autonomy support from their coaches, fathers, and mothers on the athletes' self determined motivation.   High school athletes N 335; M age 15.75 years; 62.4% female; 84.2% Caucasian) completed surveys assessing the constructs of interest near the end of their season.  Hierarchical regression analysis results showed that autonomy support from all three social agents significantly and positively predicted self determined motivation R 2 0.32) and the two and three way interactions significantly added to the prediction (total R 2 0.35) Results showed that a relatively high level of self determined motivation was associated with the perception that at least two of the three social agents provided high levels of autonomy support.  The provision of autonomy support from coaches, mothers, and fathers relate to athletes' self determined motivation both independently and interactively.
The Relationship Between Coach Expectations and Female Softball Athletes' Motivation and Perceptions of Coach Behavior

The purpose of this study was to examine the relationship between coach expectations, female athletes' perceptions of coach behavior, and motivation to play softball, and to observe changes in perceptions of behavior and motivation by expectancy group. Self determination theory (SDT; Ryan Deci, 2000) was used as a guide. Participants were randomly selected from Division I softball teams competing in the United States (n 20) Head coaches (n 20) completed evaluations rating expectations of athletes' performance ability, and athletes (n 148) self reported motivation and perceived coaching behaviors pre and post study. Cluster analysis distinguished between three expectancy groups based from coach expectation ratings: High, low, and average. Pearson's r revealed weak relationships between coach expectancy ratings, perceived coaching behaviors, and motivation. Split plot analysis of variance tests revealed expectancy groups perceived behaviors differently and were motivated differently. Low expectanc.
Towards automatic UAV data interpretation for precision farming

The EU project Flourish intends to esta blish an autonomously operating precision farming s ystem based on the interaction between unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) For ef fective mission planning and site specific ground intervent ion by the UGV, the growth, mineral nutrition, weed and health status of the crop field must be evaluated efficien tly. In this regard, the survey capabilities of UAV s can substantially leverage the economic performance and ecological su stainability of precision farming systems.  Our approach is based on 'sufficient perfo rmance ranges' (SPRs) which represent the expected optimal performance of phenotypic traits such as spectral i ndicators and growth parameters like canopy cover a nd crop height. Together with a priori data, such as weather situation and soil fertility maps, the UAV derived maps allow for the detection of deviations from sufficient crop develo pment. Detected deviations are interpreted using de cision tree models. Our models encompass upstream and downstream decisi ons necessary for scheduling site specific and effi ci nt ground interventions during the whole management sequence of a growing season, such as fertilizer input or we ed control.  This contribution presents initial results for field monitoring via UAVs. The performance of ur approach is supported by ground truth data, such as crop height canopy cover and spectral indices from sugar beet s collected in 2015. Effects of variable soil fertility, weed pres sure and drought stress are presented.  The initial results support the propose d intention to derive appropriate management decisi ons for stabilizing crop yield and quality while minimizing farm inputs.
Visual descriptors for content based retrieval of remote sensing images
Venue: ArXiv

In this article, we present an extensive evaluation of visual descriptors for the content based retrieval of remote sensing (RS) images. The evaluation includes global hand crafted, local hand crafted, and convolutional neural networks (CNNs) features coupled with four different content based image retrieval schemes. We conducted all the experiments on two publicly available datasets: the 21 class University of California (UC) Merced Land Use/Land Cover (LandUse) dataset and 19 class High resolution Satellite Scene dataset (SceneSat) The content of RS images might be quite heterogeneous, ranging from images containing fine grained textures, to coarse grained ones or to images containing objects. It is, therefore, not obvious in this domain, which descriptor should be employed to describe images having such a variability. Results demonstrate that CNN based features perform better than both global and local hand crafted features whatever is the retrieval scheme adopted. Features extracted from a residual CNN suitable fine tuned on the RS domain, shows much better performance than a residual CNN pre trained on multimedia scene and object images. Features extracted from Network of Vector of Locally Aggregated Descriptors (NetVLAD) a CNN that considers both CNN and local features, works better than others CNN solutions on those images that contain fine grained textures and objects.
Deep Image Homography Estimation
Venue: ArXiv

We present a deep convolutional neural network for estimating the relative homography between a pair of images. Our feed forward network has 10 layers, takes two stacked grayscale images as input, and produces an 8 degree of freedom homography which can be used to map the pixels from the first image to the second. We present two convolutional neural network architectures for HomographyNet: a regression network which directly estimates the real valued homography parameters, and a classification network which produces a distribution over quantized homographies. We use a 4 point homography parameterization which maps the four corners from one image into the second image. Our networks are trained in an end to end fashion using warped MS COCO images. Our approach works without the need for separate local feature detection and transformation estimation stages. Our deep models are compared to a traditional homography estimator based on ORB features and we highlight the scenarios where HomographyNet outperforms the traditional technique. We also describe a variety of applications powered by deep homography estimation, thus showcasing the flexibility of a deep learning approach.
Neural GPUs Learn Algorithms
Venue: ICLR

Learning an algorithm from examples is a fundamental problem that has been widely studied. Recently it has been addressed using neural networks, in particular by Neural Turing Machines (NTMs) These are fully differentiable computers that use backpropagation to learn their own programming. Despite their appeal NTMs have a weakness that is caused by their sequential nature: they are not parallel and are are hard to train due to their large depth when unfolded. We present a neural network architecture to address this problem: the Neural GPU. It is based on a type of convolutional gated recurrent unit and, like the NTM, is computationally universal. Unlike the NTM, the Neural GPU is highly parallel which makes it easier to train and efficient to run. An essential property of algorithms is their ability to handle inputs of arbitrary size. We show that the Neural GPU can be trained on short instances of an algorithmic task and successfully generalize to long instances. We verified it on a number of tasks including long addition and long multiplication of numbers represented in binary. We train the Neural GPU on numbers with upto 20 bits and observe no errors whatsoever while testing it, even on much longer numbers. To achieve these results we introduce a technique for training deep recurrent networks: parameter sharing relaxation. We also found a small amount of dropout and gradient noise to have a large positive effect on learning and generalization.
Dementia in Down's syndrome
Venue: The Lancet Neurology

Down's syndrome is the most common genetic cause of learning difficulties, and individuals with this condition represent the largest group of people with dementia under the age of 50 years. Genetic drivers result in a high frequency of Alzheimer's pathology in these individuals, evident from neuroimaging, biomarker, and neuropathological findings, and a high incidence of cognitive decline and dementia. However, cognitive assessment is challenging, and diagnostic methods have not been fully validated for use in these patients; hence, early diagnosis remains difficult. Evidence regarding the benefits of cholinesterase inhibitors and other therapeutic options to treat or delay progressive cognitive decline or dementia is very scarce. Despite close similarities with late onset Alzheimer's disease, individuals with Down's syndrome respond differently to treatment, and a targeted approach to drug development is thus necessary. Genetic and preclinical studies offer opportunities for treatment development, and potential therapies have been identified using these approaches.
Cost estimation method based on parallel Monte Carlo simulation and market investigation for engineering construction project
Venue: Cluster Computing

In this paper, a new cost estimation method based on parallel Monte Carlo simulation and market investigation for the chemical engineering construction project is proposed to consider both the uncertainties of cost estimation and market drivers. The critical items of exerting large impacts on the cost estimation are selected by the market investigation. Then important critical items are chosen by the sensitivity analysis based on the parallel Monte Carlo simulation combining with the Likert scale method from critical items. The Relative Important Indices and Normalized Important Indices are obtained according to the discipline and procurement experts' experience in the relative construction market. Then re rankings of market drivers will be acted as guidelines for carrying out project cost simulations based on the parallel Monte Carlo method, with inquired information of important critical items with more efficiency. An illustrative example in a petrochemical Engineering Procurement Construction contracting project in Saudi verified the validity and practicability of the proposed method.
Local and Non Local Feature Based Kernel Nonnegative Matrix Factorization Method for Face Recognition
Venue: 2016 12th International Conference on Computational Intelligence and Security (CIS)

Based on the kernel method and graph theory, this paper proposes a novel Kernel Non negative Matrix Factorization with Local and Non local feature (LN KNMF) approach for face recognition. We establish the objective function in kernel space which incorporates two scatter quantities, namely local scatter and non local scatter. They are determined by the local adjacent graph matrix and non local adjacent graph matrix respectively. The update rules of the proposed LN KNMF method are derived using polynomial kernel function and gradient descent method. Subsequently, we theoretically prove the convergence of the proposed algorithm by means of a constructed auxiliary function. Experimental results on the ORL face database demonstrate the superior performance of our approach against some NMF based methods.
Is calomel truly a poison and what happens when it enters the human stomach? A study from the thermodynamic viewpoint

The chemical and electrochemical equilibria involving mercurous and mercuric chlorides in the conditions, corresponding to the human stomach environment, were studied from the point of view of chemical thermodynamics. The Gibbs free energies of formation of various mercury and chlorine compounds, the equilibrium constants, and the electrode potentials of reactions involving these compounds at 37degC were calculated. The potential pH diagrams of Hg22+ H2O, \rm{Hg}}_2^{2 \rm{ \rm{H}}_2}\rm{O, Cl H2O, and Hg22+ Cl H2O \rm{Hg}}_2^{2 \rm{ C}\rm{l}\rm{ \rm{ \rm{H}}_2}\rm{O} systems were plotted. The question of how toxic is calomel when ingested was discussed.
Lead isotope ratio measurements as indicators for the source of lead poisoning in Mute swans (Cygnus olor) wintering in Puck Bay (northern Poland)
Venue: Chemosphere

Lead (Pb) poisoning is most commonly linked amongst anthropogenically caused deaths in waterfowl and this is often associated with hunting and fishing activities. However, the exact identification of the source may be difficult with commonly used techniques. We have studied isotope ratios using Inductively Coupled Plasma Mass Spectrometry (ICP MS) to investigate the source of Pb in the blood of Mute swans (n 49) wintering in northern Poland. We compared the values of isotopic ratios from blood and ammunition pellets available on the Polish market. The mean Pb concentrations found was 0.241 mg/g (w/w) and nearly half of the blood specimens had elevated Pb levels (higher than the cited 0.23 mg/g w/w threshold of poisoning) Only the mean 208/206 Pb isotope ratio was similar in blood and pellet samples. Mean ratios of isotopes 206/204, 206/207 and 208/207 in swans' blood and in pellets differed significantly. Moreover, coefficients of variation were higher in blood samples than in pellets. These discrepancies and significant differences in abundance of 204Pb and 207Pb isotopes in both materials indicated that pellets available today on the Polish market were not the source of Pb in the blood of Mute swans wintering in northern Poland.
L'apprentissage profond, une revolution en intelligence artificielle

Yann LeCun, specialiste de l'apprentissage automatique des machines (machine learning) est l'un des peres du Deep Learning (apprentissage profond) une methode a laquelle il se consacre depuis trente ans, malgre le scepticisme qu'il rencontre au depart dans la communaute scientifique. Le Deep Learning, qui fait appel a la fois aux connaissances en neurosciences, aux mathematiques et aux progres technologiques, est aujourd'hui plebiscite comme une veritable revolution dans le domaine de l'in.
Evaluating Playability of Mobile Games with the Expert Review Method

Expert evaluation is a widely used method for evaluating the usability of software products. When evaluating games, traditional usability heuristics lack comprehension and cannot be directly applied. In this paper, we introduce playability heuristics that are specifically designed for evaluating mobile games. Heuristics form a core model that can be used in any mobile game evaluation. The model consists of three modules: Game Usability, Mobility, and Gameplay. The mobile context has some unique characteristics, which require special attention during the evaluation. These characteristics are described in mobility heuristics. Mobile devices also set some of their own requirements for general usability and these issues are described along with game usability heuristics. These heuristics have been developed by using an iterative design process of a mobile game. In addition, we have validated the heuristics and evaluated five mobile games by using them with the expert evaluation method. The results indicate that playability problems, which violate game usability or mobility heuristics, are quite easy to identify. Gameplay problems are harder to find, but gameplay heuristics help in evaluation and focus on different aspects of the gameplay.
Rational Design of a Parthenolide based Drug Regimen That Selectively Eradicates Acute Myelogenous Leukemia Stem Cells*
Venue: The Journal of Biological Chemistry

Although multidrug approaches to cancer therapy are common, few strategies are based on rigorous scientific principles. Rather, drug combinations are largely dictated by empirical or clinical parameters. In the present study we developed a strategy for rational design of a regimen that selectively targets human acute myelogenous leukemia (AML) stem cells. As a starting point, we used parthenolide, an agent shown to target critical mechanisms of redox balance in primary AML cells. Next, using proteomic, genomic, and metabolomic methods, we determined that treatment with parthenolide leads to induction of compensatory mechanisms that include up regulated NADPH production via the pentose phosphate pathway as well as activation of the Nrf2 mediated oxidative stress response pathway. Using this knowledge we identified 2 deoxyglucose and temsirolimus as agents that can be added to a parthenolide regimen as a means to inhibit such compensatory events and thereby further enhance eradication of AML cells. We demonstrate that the parthenolide, 2 deoxyglucose, temsirolimus (termed PDT) regimen is a potent means of targeting AML stem cells but has little to no effect on normal stem cells. Taken together our findings illustrate a comprehensive approach to designing combination anticancer drug regimens.
A Rational Strategy for the Use of Amniotic Epithelial Stem Cell Therapy for Liver Diseases
Venue: Stem cells translational medicine

Stem cell based therapies hold the potential to alleviate the burden of many serious diseases, including those of the liver. Among the different types of stem cells, human placenta derived stem cells are potentially one of the most clinically applicable stem cells because of their tissue specific advantages. They are a readily available cell source that can be procured in a noninvasive manner, and there are few ethical concerns regarding their use. Recent studies have demonstrated that the amniotic epithelium contains stem cells that possess four unique and advantageous properties; human amniotic epithelial cells (hAECs) have low immunogenicity, secrete several immune regulatory molecules, possess the potential to differentiate into all three germ layers, and contain abundant lysosomes allowing them to secrete lysosomal enzymes. This perspective article provides an overview of the beneficial properties of hAECs and proposes a rational strategy for translating placental stem cells toward clinical application for various liver diseases.