Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolving Institutions and Catching-up by the Candidate Countries of the EU

Since institutions include not only governmental and civic organizations and regulations, but also social customs and behavior, the post-transition development can not be analyzed or understood without the heritage of the past. All-encompassing state-ownership, monopolization and elimination of all kinds of competition reached unprecedented proportions in the Soviet-type economies, creating oligarchic organizations, mentalities, and behavioral patterns. The ruling communist elite enjoyed extreme privileges; citizens became much more alienated from the state than in democratic societies. Experiences of the fastest growing countries proved that by reforming the institutional system and the policy-regime the growth potential of an economy can be much better exploited and catching-up with the more developed countries can be faster. Because of the "velvet" character of transition in East Central Europe, many of the old special interest organizations were not destroyed, thus creating obstacles to faster growth and structural change. In the catch-up process much depends on the openness of an economy, however its performance is basically determined endogenously, by the allocation of resources and their efficient use depending on the institutional structure. The lack or weakness of the rule of the law is one of the main obstacles to the economic catching-up of the East Central European societies and their accession to the European Union. The share of the shadow economy in the transition countries increased very substantially and is about twice as big as in developed market economies. Increased criminal activities and large-scale tax-avoidance is closely related to corruption and is very damaging for the lawful and secure environment of growth. Corruption is strongly negatively associated with the investment rate, and through this it lowers the rate of growth. Clientelistic networks evolved around the political parties, creating a social network where political loyalty prevails against market relations, democratic decision making, and professionalism in public administration. In the political struggles, the question was not only which political group would govern the country, but also who would privatize the vast quantity of state assets, and to whom. In clientelist regimes corruption, favoritism, partiality, give-and-take, and nepotism abound, and thus democratic morale and the logic of the market is destroyed. The analysis shows that the institutions in place in most of the East Central European countries are suitable to achieve a faster growth rate than the EU average, thus catching-up in the not too distant future seems probable after accession. However, the deficiencies in the rule of law are seriously hindering the full utilization of the growth potential of these economies.
Direct Insertion of MODIS Radiances in a Global Aerosol Transport Model

Abstract In this paper results are presented from a simple offline assimilation system that uses radiances from the Moderate Resolution Imaging Spectroradiometer (MODIS) channels that sense atmospheric aerosols over land and ocean. The MODIS information is directly inserted into the Goddard Chemistry and Aerosol Radiation Transport model (GOCART), which simulates the following five aerosol types: dust, sea salt, black carbon, organic carbon, and sulfate. The goal is to produce three-dimensional fields of these aerosol types for radiative forcing calculations. Products from this assimilation system are compared with ground-based measurements of aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Insertion of MODIS radiances draws the GOCART model closer to the AERONET AOD. However, there are still uncertainties with surface reflectivity over moderately bright surfaces and with the amount of absorbing aerosol. Also described is the assimilation cycle. The forward model takes the aerosol .
Research on one-class JPEG steganalysis based on dimensionality-reduced correlation features

This paper proposed a novel one-class steganalysis method to blindly detect the existence of hidden messages in JPEG images. We used the co-occurrence matrix to capture the correlations among neighboring coefficients in both Discrete Cosine Transform (DCT) domain and Discrete Wavelet Transform (DWT) domain. Then the correlation features were calibrated, their dimensionality was reduced by Locality Preserving Projection (LPP) method and a one-class Support Vector Data Description (SVDD) classifier was trained to make classification. The new method trained only on examples of cover images. The results show that it's able to afford reasonable accuracy to distinguish between cover and stego images. Furthermore, LPP method is much better than Principal Components Analysis (PCA) method for improving the algorithm's classification accuracy.
Two-scale computation for 2-order elliptic problem with periodically oscillating coefficients

In this paper, a new approach for constructing the two-order and two-scale approximate solution to the elliptic problem with the sharply periodic oscillation coefficients is developed. The results of many numerical experiments for different two-order and two-scale approaches, in those the one-order local solution and two-order local solution defined in reference cell respectively satisfy different combination of boundary conditions are shown.
Strategy as Improvisational Theater

In following the traditional model of strategy development, a company seeks to craft the best possible plan so that it can be handed off for a predetermined course of execution involving a predictable set of events and a specific final goal. This scripted approach resembles traditional theater: The actors speak the same lines and the action comes to the same satisfying conclusion, night after night. The model works well when business is going through a relatively stable period. The current situation, however, is not stable: Companies are still trying to navigate the technological tsunami created by the Internet. Under the circumstances, it makes much more sense for companies to follow an improvisational model ? that is, to throw out the script, bring in the audience and trust the actors to innovate on the spot. The metaphor of improvisational theater helps executives think about the way in which an entire organization can become an arena for staging experiments that can transform a company's overarching strategy. Harvard Business School professor Rosabeth Moss Kanter lays out the six elements of strategic improvisation and illustrates how companies have made use of each one to get the most out of new technologies. Senior managers who understand these elements can create an atmosphere in which improvisational theater thrives; change then becomes an organic process rather than a painful reaction to circumstances beyond the company's control. Although this approach does not advocate a big plunge into something totally new, it is anything but conservative. Companies that engage in continual improvisation through innovative projects of all sizes and shapes are much better equipped to explore highly threatening disruptive technologies and embrace quite radical changes.
Are internet firms different? : evidence from insider trading

This study investigates whether the information content of insider transactions, with a focus on sell transactions, is different for high growth, high volatility Internet-based firms. Prior research on more “traditional” firms has found a small, but significant negative abnormal return with insider sells, which points to an association of insider sells with negative information about the firm by outsiders. We employ several models to examine over 1,000 inside transactions for more than 100 NETDEX firms to find that for Internet firms, insider sells are not followed by a significant negative abnormal return. Firm size effects differ between the different methods employed. In conclusion, it appears that while insider sales in traditional firms are motivated by information asymmetry reason, insider sales in Internet firms are not. We conclude that Internet firms are different indeed.
Expressiveness of spatial logic for trees

In this paper we investigate the quantifier-free fragment of the TQL logic proposed by Cardelli and Ghelli. The TQL logic, inspired from the ambient logic, is the core of a query language for semistructured data represented as unranked and unordered trees. The fragment we consider here, named STL, contains as main features spatial composition and location as well as a fixed point construct. We prove that satisfiability for STL is undecidable. We show also that STL is strictly more expressive than the Presburger monadic second-order logic (PMSO) of Seidl, Schwentick and Muscholl when interpreted over unranked and unordered edge-labelled trees. We define a class of tree automata whose transitions are conditioned by arithmetical constraints; we show then how to compute from a closed STL formula a tree automaton accepting precisely the models of the formula. Finally, still using our tree automata framework, we exhibit some syntactic restrictions over STL formulae that allow us to capture precisely the logics MSO and PMSO.
Measuring Cellular Immunity to Influenza: Methods of Detection, Applications and Challenges

Influenza A virus is a respiratory pathogen which causes both seasonal epidemics and occasional pandemics; infection continues to be a significant cause of mortality worldwide. Current influenza vaccines principally stimulate humoral immune responses that are largely directed towards the variant surface antigens of influenza. Vaccination can result in an effective, albeit strain-specific antibody response and there is a need for vaccines that can provide superior, long-lasting immunity to influenza. Vaccination approaches targeting conserved viral antigens have the potential to provide broadly cross-reactive, heterosubtypic immunity to diverse influenza viruses. However, the field lacks consensus on the correlates of protection for cellular immunity in reducing severe influenza infection, transmission or disease outcome. Furthermore, unlike serological methods such as the standardized haemagglutination inhibition assay, there remains a large degree of variation in both the types of assays and method of reporting cellular outputs. T-cell directed immunity has long been known to play a role in ameliorating the severity and/or duration of influenza infection, but the precise phenotype, magnitude and longevity of the requisite protective response is unclear. In order to progress the development of universal influenza vaccines, it is critical to standardize assays across sites to facilitate direct comparisons between clinical trials.
An Assessment of Self-Organizing Maps and k-means Clustering Approaches for Atmospheric Circulation Classification

— This study presents an analysis and comparison between the application of self-organizing maps (SOM) and the k-means clustering approaches in the field of atmospheric circulation classification, focusing in the area of southeastern Europe. Circulation type classification is a significant aspect of climate research in terms of examining the large-scale atmospheric variability and its relationship with local climate parameters. The study utilizes mean daily sea level pressure (MSLP) data for the spring months of a 62-year period (1948 to 2009) on a grid with 2.5 o x2.5 o resolution. Both schemes provide realistic classifications, differentiating in the number of the resulting circulation patterns. The two methods are compared by examining the distribution of each SOM circulation type members (days) to every k-means type and by investigating the pressure field correspondence along with their frequencies of occurrence. High similarity is observed, especially for the patterns where atmospheric circulation is controlled from high-pressure barometric systems. The SOM method is found to be superior, due to its ability to generate a non-linear classification and produce a map where closely related atmospheric modes are described by neighboring neurons and positioned in adjacent locations.
HIGHER-ORDER NONLINEARITY IN ACCRETION DISKS: QUASI-PERIODIC OSCILLATIONS OF BLACK HOLE AND NEUTRON STAR SOURCES AND THEIR SPIN

We propose a unified model to explain Quasi-Periodic Oscillation (QPO), particularly of high frequency, observed from black hole and neutron star systems globally. We consider accreting systems to be damped harmonic oscillators exhibiting epicyclic oscillations with higher-order nonlinear resonance to explain QPO. The resonance is expected to be driven by the disturbance from the compact object at its spin frequency. The model explains various properties parallelly for both types of the compact object. It describes QPOs successfully for ten different compact sources. Based on this, we predict the spin frequency of the neutron star Sco X-1 and specific angular momentum of black holes GRO J1655−40, XTE J1550−564, H1743−322, and GRS 1915+105.
The OAI2LOD Server: Exposing OAI-PMH Metadata as Linked Data

Many institutions grant access to their metadata repositories via the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). However, this protocol has two signicant drawbacks: it does not make its resources accessible via dereferencable URIs, and it provides only restricted means of selective access to metadata. The OAI2LOD Server handles these shortcomings by republishing metadata originating from an OAI-PMH endpoint according to the principles of Linked Data. As the ongoing OAI-ORE specication process shows, these principles are gaining growing importance also in the digital libraries domain.
Large Wildfires and Wildfire Costs : How Much and Why ?

A quantitative tool was developed to predict USDA Forest Service fire suppression expenditures by fiscal year on the basis of fire activity data (i.e., number of fires and acres burned) from Incident Management Situation Reports. Regional regression models were developed with adjusted rsquares ranging from 0.696 to 0.969. National predictions result from aggregating regional predictions. Predictions are made monthly during the fire season, starting in June. Subsequent predictions reflect actual, past expenditure information and the level of fire activity likely to occur in the remaining months of the fiscal year. This tool was used by Fire and Aviation Management to predict fire suppression expenditures for fiscal year 1998. Since fiscal year (FY) 1970, fire suppression has accounted for more than half of all USDA Forest Service (FS) fire-related expenditures (Schuster and others 1997). Generally, appropriated and emergency suppression funds are enough to support fire suppression activities; however, when extraordinary fire seasons occur, funds held in reserve for other purposes (such as Knutson-Vandenberg Funds) must be borrowed to pay the extra costs. Concern has been expressed in recent years that fire suppression expenditures have been increasing, making it even more difficult to appropriate an adequate amount of suppression funds. As part of the annual appropriation process, the FS provides Congress with an estimate of the amount of money that will be needed to suppress fires for the current fiscal year. Starting in June each year, Fire and Aviation Management (FAM) must provide the office of the Chief of the Forest Service and the Office of Management and Budget (OMB) with monthly, up-to-date predictions of total FY fire suppression expenditures. Because it is unknown what the upcoming fire season will entail, predicting expenditures is a difficult task, subject to error. Simply averaging the expenditures from previous years may be too general for an acceptable prediction. On the other hand, a detailed, accounting-based prediction may be far too complicated and time consuming. Another approach might be to increase predictions of fire suppression expenditures by some factor, say 20 percent. However, how does one decide upon the appropriate adjustment? Should it be 10 percent, 20 percent, or 30 percent? In a year as extreme as FY 1994, with fire suppression expenditures of $685 million, one would have needed to add a factor of 218 percent to the 1990-94 average of $215 million to arrive at an accurate prediction. In an attempt to improve their predictions, FAM requested the development of a tool for predicting fire suppression expenditures. This study developed a quantitative tool for predicting annual, fire suppression expenditures that can be updated every month, starting in June, making use of available fire information and yearto-date expenditure information.
Representative elementary volume estimation for porosity, moisture saturation, and air‐water interfacial areas in unsaturated porous media: Data quality implications

Achieving a representative elementary volume (REV) has become a de facto criterion for demonstrating the quality of CT measurements in porous media systems. However, the data quality implications of an REV requirement have not been previously examined. In this work, deterministic REVs for porosity, moisture saturation (SW), and air‐water interfacial area (AI) were estimated using a set of 49 CT images of eight unsaturated homogeneous porous media with heterogeneity in moisture distributions present in varying degrees. Estimated porosity REVs were <8 mm3 for all cases, smaller than typical CT image sizes (∼100 mm3). Estimated SW and AI REVs were <55 mm3 for cases with homogeneous moisture distributions but could not be estimated for cases with heterogeneous moisture distributions, due to the absence of a distinct “REV plateau” within the maximum imaged volume. Conventionally, SW and AI data from such non‐REV cases would be excluded. The implications of excluding data on the basis of REV were examined by comparing AI‐SW data measured on image windows of increasing size against the expected linear AI‐SW relationship. At measurement scales exceeding porosity REV, random fluctuations in AI‐SW data were excluded, even for cases containing heterogeneous moisture distributions. In contrast, requiring measurement scales to exceed SW and AI REV appeared overly restrictive and resulted in visible loss of reliable AI‐SW data. We attribute these findings to overestimation of REVs due to inherently problematic estimation of deterministic REVs in real systems. Implications of these findings for ensuring CT data quality and the efficient use of CT data are discussed.
Responses of rice cultivars with different nitrogen use efficiency to partial nitrate nutrition.

BACKGROUND AND AIMS There is increased evidence that partial nitrate (NO3-) nutrition (PNN) improves growth of rice (Oryza sativa), although the crop prefers ammonium (NH4+) to NO3- nutrition. It is not known whether the response to NO3- supply is related to nitrogen (N) use efficiency (NUE) in rice cultivars. Methods Solution culture experiments were carried out to study the response of two rice cultivars, Nanguang (High-NUE) and Elio (Low-NUE), to partial NO3- supply in terms of dry weight, N accumulation, grain yield, NH4+ uptake and ammonium transporter expression [real-time polymerase chain reaction (PCR)]. KEY RESULTS A ratio of 75/25 NH4+ -N/NO3- -N increased dry weight, N accumulation and grain yield of 'Nanguang' by 30, 36 and 21 %, respectively, but no effect was found in 'Elio' when compared with those of 100/0 NH4+ -N/NO3- -N. Uptake experiments with 15N-NH4+ showed that NO3- increased NH4+ uptake efficiency in 'Nanguang' by increasing Vmax (14 %), but there was no effect on Km. This indicated that partial replacement of NH4+ by NO3- could increase the number of the ammonium transporters but did not affect the affinity of the transporters for NH4+. Real-time PCR showed that expression of OsAMT1s in 'Nanguang' was improved by PNN, while that in 'Elio' did not change, which is in accordance with the differing responses of these two cultivars to PNN. Conclusions Increased NUE by PNN can be attributed to improved N uptake. The rice cultivar with a higher NUE has a more positive response to PNN than that with a low NUE, suggesting that there might be a relationship between PNN and NUE.
Separate and Not Equal ? Gender Segregation in the Labor Market and the Gender Wage Gap

1200 18th Street, NW ♦ Suite 301 ♦ Washington, DC 20036 ♦ (202) 785-5100 ♦ www.iwpr. org Occupational gender segregation is a strong feature of the US labor market. While some occupations have become increasingly integrated over time, others remain highly dominated by either men or women. Our analysis of trends in overall gender segregation shows that, after a considerable move towards more integrated occupations in the 1970s and 1980s, progress has completely stalled since the mid 1990s. Occupational segregation is a concern to policy makers for two reasons: it is inefficient economically, preventing able people from moving into occupations where they could perform well and that would satisfy them more than the ones open to them. And occupational segregation is a major cause for the persistent wage gap. Our analysis confirms that average earnings tend to be lower the higher the percentage of female workers in an occupation, and that this relationship is strongest for the most highly skilled occupations, such as medicine or law. Yet this is also a strong feature of jobs requiring little formal education and experience, increasing the likelihood of very low earnings for women working in female-dominated, low-skilled occupations such as childcare. Separate and Not Equal? Gender Segregation in the Labor Market and the Gender Wage Gap
Portfolio Selection with Parameter and Model Uncertainty ∗

In this paper, we extend the mean-variance portfolio model to explicitly account for uncertainty about the estimated expected returns and/or the underlying return-generating model. We do this by first imposing an additional constraint on the mean-variance portfolio optimization program that restricts each parameter to lie within a specified confidence interval of its estimated value, and then by minimizing over the choice of parameters and/or models subject to this constraint, in addition to the maximization over portfolio weights. Our model has several attractive features. One, it is firmly grounded in decision theory. Two, it is flexible enough to allow for uncertainty about expected returns estimated jointly for all assets or different levels of uncertainty about expected returns for different subsets of the assets. The estimation may be undertaken using classical estimators based on sample moments or a factor model or using Bayesian methods. Three, we show that the max-min problem can be reduced to just a maximization that has the same complexity as the classical mean-variance problem. We demonstrate our approach using MSCI data on equity index returns for eight countries. We find that the out-of-sample returns generated by our portfolio model have a higher mean and lower volatility compared to those from a standard mean-variance model, and to those from Bayesian models.
A Fuzzy-based Trust Management in WSNs

Wireless sensor networks (WSNs) are composed of a large number of sensor nodes that are selforganized. The open nature of WSNs makes them easily exposed to a variety of attacks and brings many security challenges. Furthermore, because of the potentially dynamic behavior of WSNs, there is hardly any infrastructure or centralized control. To enhance the security in WSNs, it is necessary to evaluate the trustworthiness of nodes since some of them may refuse to forward packets for saving their limited resources. In this paper, we propose a fuzzy-based trust management in WSNs. All neighbors of one nodes are ranked by their trust value. Only those with higher trust values can be chosen to forward packets. Our trust model exploits multiple metrics, rather than a single metric, and converts them into a single numerical ranking. We perform an thorough evaluation of our proposed approach. The results indicate that our trust model can not only identify abnormal nodes’ behaviors, but can also reduce query traffic while improving the reliability of the exchange messages.