nils-herrmann/clean_scirepeval huggingface dataset:
Prospective Analysis of Life Cycle Indicators through Endogenous Integration into a National Power Generation Model

Given the increasing importance of sustainability aspects in national energy plans, this article deals with the prospective analysis of life cycle indicators of the power generation sector through the case study of Spain. A technology rich, optimisation based model for power generation in Spain is developed and provided with endogenous life cycle indicators (climate change, resources, and human health) to assess their evolution to 2050. Prospective performance indicators are analysed under two energy scenarios: a business as usual one, and an alternative scenario favouring the role of carbon dioxide capture in the electricity production mix by 2050. Life cycle impacts are found to decrease substantially when existing fossil technologies disappear in the mix (especially coal thermal power plants) In the long term, the relatively high presence of natural gas arises as the main source of impact. When the installation of new fossil options without CO2 capture is forbidden by 2030, both renewable technologies and to a lesser extent fossil technologies with CO2 capture are found to increase their contribution to electricity production. The endogenous integration of life cycle indicators into energy models proves to boost the usefulness of both life cycle assessment and energy systems modelling in order to support decision and policy making.
Prospective Analysis Of The Algerian Economic Growth By 2025: Structural Analysis

The aim of this study is to assess the economic growth in Algeria and to determine the set of variable impacting it on different periods. It relies on structural analysis approach based on an investigation method using the software MICMAC (matrix of cross influence, applied multiplication to a ranking) which was developed by the Institute of Computing Innovation 3IE following the request of Investigation Laboratory on Prospective, Strategy and Organization LIPSOR. The structural analysis can define the key variables of the system (VCS) to catch the most influential variables on economic growth during three time intervals: the near past (direct impact) the actual period (indirect influence) and the long run (potential indirect impact) bearing in mind that the horizon of our study is 2025. The results show the most influential variables ranked during each period as follows: The near past (the direct influence) the economic system, public spending, the regulation, foreign reserves and price of oil barrel, unemployment, inflation and SMEs. The actual period (the indirect influence) the economic system, business climate, FDI, the price of the oil barrel, active population, occupied population, external debt. In addition to other variables having a less influence: unemployment, SMEs, inflation and foreign trade. The long run (potential indirect influence) by 2025: The economic system, public spending, the regulation, foreign reserves, unemployment, FDI, inflation, business climate, currency, occupied population, the price of the oil barrel, saving. Other variables have a moderate influence: national security, capital, exchange rate, financial system, active population, IT, informal sector, SMEs, external trade, external debt, demographic growth and the interest rate.
Endogeneity Analysis of Output Synchronization in the Current and Prospective EMU

The sustainability of European EMU (economic and monetary union) remains an important issue in light of existing plans for enlargement. This article conducts an endogeneity analysis of output synchronization, based on panel data estimation from 1994 to 2013, for different country groups, including core, periphery, central and eastern European countries, northern European countries and the prospective candidate countries, which are expected to adopt the euro over the coming years. The quantification of trade related and direct spillover channels associated with monetary integration provides insight into the relative importance of direct and indirect synchronization gains arising from EMU membership. The use of amplitude and concordance measures of synchronization and a range of estimators enhances robustness. Important endogeneity implications emerge from our analysis.
Prospective financial analysis with regard to enterprise risk exposure the advantages of the Monte Carlo method

Prospective financial analysis is a key decision tool in an enterprise. The traditional approach confronts the forecasted value of a financial category or a financial ratio with a requirement or a standard. Knowing that the particular category or the ratio meets the requirement or the standard is a kind of risk information, but realizing that the requirement or the standard is met with a particular probability level is a detailed image of risk. The aim of the paper is to indicate the possibility to increase the effectiveness of prospective financial analysis by using a Monte Carlo simulation. The biggest advantage of the presented approach (that is in fact the evolution of the traditional scenario approach to risk analysis) is that it delivers the detailed probability distributions of key financial categories and ratios. Shareholders accepting the results of prospective financial analysis with the Monte Carlo simulation should accept risk in a more conscious way than in the case of the traditional approach
Exploring Biomarkers for Alzheimer's Disease.
Venue: Journal of clinical and diagnostic research JCDR

Alzheimer's Disease (AD) is one of the most common form of dementia occurring in elderly population worldwide. Currently Ab42, tau and p tau in the cerebrospinal fluid is estimated for confirmation of AD. CSF which is being used as the potent source for biomarker screening is obtained by invasive lumbar punctures. Thus, there is an urgent need of minimal invasive methods for identification of diagnostic markers for early detection of AD. Blood serum and plasma serves as an appropriate source, due to minimal discomfort to the patients, promoting frequent testing, better follow up and better consent to clinical trials. Hence, the need of the hour demands discovery of diagnostic and prognostic patient specific signature biomarkers by using emerging technologies of mass spectrometry, microarrays and peptidomics. In this review we summarize the present scenario of AD biomarkers such as circulatory biomarkers, blood based amyloid markers, inflammatory markers and oxidative stress markers being investigated and also some of the potent biomarkers which might be able to predict early onset of Alzheimer's and delay cognitive impairment.
Environmental emissions at foundation construction stage of buildings Two case studies

Foundation construction involves heavy machine usage which contributes to greenhouse gas (GHG) and non GHG emissions. The study aims to develop a model to estimate and compare emissions at foundation construction and demonstrate its application using two case studies. A process based quantitative method is established to estimate emissions due to materials, transportation, and equipment usage. The results are analysed under five impact categories including Global Warming Potential, Acidification Potential, Eutrophication Potential, Photochemical Oxidant Formation Potential and Human Toxicity Potential. Analytical Hierarchy Process is employed to obtain weighting factors to assess impact categories under global and local perspectives. Results obtained an average GHG emission of 67% 19% and 14% from materials, equipment and transportation respectively. This observation signifies the relative higher percentage of emission distribution of equipment and transportation in foundation construction compared to that in the total building construction. Considerable amount of non GHG emissions such as Nitrous Oxides and Carbon Monoxides were recorded. Global Warming Potential remained the most prominent impact potential from all the perspectives considered, with an overpowering 75% contribution from global perspective. However, this relative importance is reduced to 33.74% 34.85% with a relative increase in Photochemical Oxidant Formation and Eutrophication Potentials to 32.55% and 31.92% at regional and local perspective. Therefore emissions such as Nitrogen Oxides, Carbon Monoxides and Sulphur Dioxide should be given more consideration at the regional and local perspectives. Results also convey that the emission comparison perspective could change the focus of environmental impacts considerably.
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Venue: NIPS

We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. Our method iteratively transports a set of particles to match the target distribution, by applying a form of functional gradient descent that minimizes the KL divergence. Empirical studies are performed on various real world models and datasets, on which our method is competitive with existing state of the art methods. The derivation of our method is based on a new theoretical result that connects the derivative of KL divergence under smooth transforms with Stein's identity and a recently proposed kernelized Stein discrepancy, which is of independent interest.
Proposing and assessing a software visualization approach based on polymetric views
Venue: J. Vis. Lang. Comput.

In this paper, we present an approach for the visualization of object oriented software. This approach has been implemented in MetricAttitude, a visualization tool based on static analysis that provides a mental picture of a software implemented in Java by means of polymetric views. The approach graphically represents a suite of object oriented design metrics (e.g. Weighted Methods per Class) and "traditional" code size metrics (e.g. Lines Of Code) To assess the validity of our proposal, we have conducted two users' studies with students in Computer Science and professional software developers. The used empirical method is qualitative. To assess MetricAttitude and its underlying approach, we conducted questionnaire based surveys. Results suggest that MetricAttitude is a viable means to deal with existing objects oriented software and to comprehend their source code, in particular. HighlightsWe propose an approach for the visualization of object oriented software.A software system is represented by polymetric views relied on statistical analysis.A software system overview in terms of size, complexity and structure is provided.Classes candidated for refactoring, important classes and hierarchies are highlighted.Two user studies with students and professional software developers are discussed.
Software engineering education learning process for professional developers

In this modern era, software projects have been developed by more than one developer that may be on different division or even different organizations. This approach is already known as Join Application Development (JAD) The main problem on JAD is the effectiveness of collaboration and communication between developers. In software engineering, the communication and collaboration can be facilitated through a good project management and IT infrastructure. However, the approach still has challenges in the process of learning and knowledge sharing. For example, how to make sure that the developers have suf cient knowledge on a project. Furthermore, how developers know what they need to learn and what they need to share with the team members. This paper will propose an innovative teaching model for a software engineering education through software engineering education learning process on the organization. Software engineering education learning process will facilitate the developer team to identify, to learn, and to validate the software engineering education that needed to nish a software project. This model will utilize software development lifecycle (SDLC) as a timeline to integrate between learning process and project execution. The proposed model can be bene cial for project manager to make sure that the developers have suf cient knowledge to nish the project effectively.
Observations on Knowledge Transfer of Professional Software Developers during Pair Programming
Venue: 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE C)

Software development is knowledge intense work, and so is pair programming. However, the importance of knowledge transfer in pair programming is usually only stressed for expert novice constellations.  Understand how knowledge transfer during pair programming works and eventually provide guidance for practitioners.  Detailed qualitative data analysis of full length recordings of industrial pair programming sessions.  Expert software developers need to transfer knowledge, too, in order to conduct productive pair programming sessions. There is a diversity of beneficial and potentially problematic patterns, which even good pairs do not steadily apply or avoid, respectively.  Pair programming is a versatile practice that even experts can profit from. Knowledge transfer skills do not automatically emerge from good software development skills, but can probably be learned.
Buffer Stock Saving and Households' Response to Income Shocks

We use the Italian Survey of Household Income and Wealth, a rather unique dataset with a long time dimension of panel information on consumption, income and wealth, to structurally estimate a buffer stock saving model. We exploit the information contained in the joint dynamics of income, consumption and wealth to quantify the degree of insurance against income risk. The estimated model implies that Italian households can insure between 89 and 95 percent of a transitory and between 7 and 9 percent of a permanent income shock. Compared to existing empirical estimates for the same dataset, our findings suggest that Italian households do not have access to significant insurance beyond self insurance.
Buffer Stock Saving and Households' Response to Income Shocks

We use the Italian Survey of Household Income and Wealth, a rather unique dataset with a long time dimension of panel information on consumption, income and wealth, to structurally estimate a buffer stock saving model. We exploit the information contained in the joint dynamics of income, consumption and wealth to quantify the degree of insurance against income risk. The estimated model implies that Italian households can insure between 89 and 95 percent of a transitory and between 7 and 9 percent of a permanent income shock. Compared to existing empirical estimates for the same dataset, our findings suggest that Italian households do not have access to significant insurance beyond self insurance
Buffer stock savings in a New Keynesian business cycle model

We introduce the tractable buffer stock savings setup of Carroll (2009 NBER Working Paper) into an otherwise conventional New Keynesian dynamic stochastic general equilibrium model with financial frictions. The introduction of a precautionary saving motive arising from an uninsurable risk of permanent income loss, affects the model's properties in a number of interesting ways: it produces a more hump shaped reaction of consumption in response to both supply (technology) and demand (monetary) shocks, and more pronounced reactions in response to demand shocks. Adoption of the buffer stock savings setup thus offers a more microfounded way, compared to, e.g. habit preferences in consumption, to introduce Keynesian features into the model, serving as a device to curbing excessive consumption smoothing, and to attributing a higher role to demand driven fluctuations. We also discuss steady state effects, determinacy properties as well as other practical issues. (authors' abstract)
New Inorganic Organic Hybrid Materials and Their Oxides for Removal of Heavy Metal Ions: Response Surface Methodology Approach
Venue: Journal of Inorganic and Organometallic Polymers and Materials

In this paper, Cu(II) Fe(III) Pb(II) and Zn(II) heavy metal ions were removed from their aqueous solutions by using novel inorganic organic hybrid materials, Al GPTS H and Al GPTS NaOSiMe3 H (hybrid material 1 and 2, respectively) and their oxides (calcined 1 and 2) as adsorbents. These ions removal by adsorption was optimized by using response surface methodology (RSM) Central composite design (CCD) method was used in order to investigate the effects of initial pH, initial metal concentration of solutions and adsorbent quantity on the adsorption efficiency (R, As a result of the experiments under optimum conditions, the maximum R values were obtained by hybrid material 1 for Fe(III) (99.89% and by calcined material 1 for Pb(II) (97.14% respectively. These quite high adsorption efficiency values have shown that these hybrid materials and their oxides are suitable to use for heavy metal ions removal from aqueous solutions.
The microbiota derived metabolite indole decreases mucosal inflammation and injury in a murine model of NSAID enteropathy
Venue: Gut microbes

Non steroidal anti inflammatory drugs (NSAIDs) are one of the most frequently used classes of medications in the world. Unfortunately, NSAIDs induce an enteropathy associated with high morbidity and mortality. Although the pathophysiology of this condition involves the interaction of the gut epithelium, microbiota, and NSAIDs, the precise mechanisms by which microbiota influence NSAID enteropathy are unclear. One possible mechanism is that the microbiota may attenuate the severity of disease by specific metabolite mediated regulation of host inflammation and injury. The microbiota derived tryptophan metabolite indole is abundant in the healthy mammalian gut and positively influences intestinal health. We thus examined the effects of indole administration on NSAID enteropathy. Mice (n 5 per group) were treated once daily for 7 days with an NSAID (indomethacin; 5 mg/kg) indole (20 mg/kg) indomethacin plus indole, or vehicle only (control) Outcomes compared among groups included: microscopic pathology; fecal calprotectin concentration; proportion of neutrophils in the spleen and mesenteric lymph nodes; fecal microbiota composition and diversity; small intestinal mucosal transcriptome; and, fecal tryptophan metabolites. Co administration of indole with indomethacin: significantly reduced mucosal pathology scores, fecal calprotectin concentrations, and neutrophilic infiltration of the spleen and mesenteric lymph nodes induced by indomethacin; modulated NSAID induced perturbation of the microbiota, fecal metabolites, and inferred metagenome; and, abrogated a pro inflammatory gene expression profile in the small intestinal mucosa induced by indomethacin. The microbiota derived metabolite indole attenuated multiple deleterious effects of NSAID enteropathy, including modulating inflammation mediated by innate immune responses and altering indomethacin induced shift of the microbiota.
NSAID induced Gastrointestinal Injury: A Focused Update for Clinicians.
Venue: Journal of clinical gastroenterology

Gastroenterologists care for users of nonsteroidal anti inflammatory drugs (NSAIDs) when the vast population exposed to the medication class experiences a relatively uncommon serious gastrointestinal (GI) side effect. As serious adverse cardiovascular (CV) effects of these drugs have also been recognized, there remains continued confusion about the best treatment for patients who benefit from NSAID therapy and are at risk for GI and CV adverse events. Recognition of those patients at risk and strategies to reduce the adverse side effects of NSAIDs continues to provide an opportunity to improve patient outcomes. This review discusses the injury induced by these agents throughout the GI tract as well as strategies to prevent acute injury and reduce the development of serious adverse events. NSAID medication selection as well as GI cotherapy should balance individual patients' GI and CV risks.
Transfer Learning for Low Resource Neural Machine Translation
Venue: EMNLP

The encoder decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low resource languages. We present a transfer learning method that significantly improves Bleu scores across a range of low resource languages. Our key idea is to first train a high resource language pair (the parent model) then transfer some of the learned parameters to the low resource pair (the child model) to initialize and constrain training. Using our transfer learning method we improve baseline NMT models by an average of 5.6 Bleu on four low resource language pairs. Ensembling and unknown word replacement add another 2 Bleu which brings the NMT performance on low resource machine translation close to a strong syntax based machine translation (SBMT) system, exceeding its performance on one language pair. Additionally, using the transfer learning model for re scoring, we can improve the SBMT system by an average of 1.3 Bleu, improving the state of the art on low resource machine translation.
Unsupervised Cross Dataset Transfer Learning for Person Re identification
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Most existing person re identification (Re ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. This severely limits their scalability in realworld applications. To overcome this limitation, we develop a novel cross dataset transfer learning approach to learn a discriminative representation. It is unsupervised in the sense that the target dataset is completely unlabelled. Specifically, we present an multi task dictionary learning method which is able to learn a dataset shared but target data biased representation. Experimental results on five benchmark datasets demonstrate that the method significantly outperforms the state of the art.
Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement
Venue: SIGIR

Online metrics measured through A/B tests have become the gold standard for many evaluation questions. But can we get the same results as A/B tests without actually fielding a new system? And can we train systems to optimize online metrics without subjecting users to an online learning algorithm? This tutorial summarizes and unifies the emerging body of methods on counterfactual evaluation and learning. These counterfactual techniques provide a well founded way to evaluate and optimize online metrics by exploiting logs of past user interactions. In particular, the tutorial unifies the causal inference, information retrieval, and machine learning view of this problem, providing the basis for future research in this emerging area of great potential impact. Supplementary material and resources are available online at http:/www.cs.cornell.edu/~adith/CfactSIGIR2016.