Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypothetical surveys versus real commitments: further evidence

The contingent valuation method is frequently used to estimate willingness to pay for environmental services. However, numerous studies have raised serious questions about the validity and reliability of such estimates. The study reported here extends the analysis of hypothetical versus real commitments by considering the effects of alternative indicators of WTP in the hypothetical setting. While part of the results confirm the findings of previous studies–CV methods can result in an overestimate of actual mean WTP–evidence is also produced that a carefully designed CV instrument can produce an estimate of mean WTP that is much closer to actual mean WTP than previous studies would suggest.
Survey Response‐Related Biases in Contingent Valuation: Concepts, Remedies, and Empirical Application to Valuing Aquatic Plant Management

Sample nonresponse and selection biases that may occur in survey research such as contingent valuation applications are discussed and tested. Correction mechanisms for these types of biases are demonstrated. Results indicate the importance of testing and correcting for unit and item nonresponse bias in contingent valuation survey data. When sample nonresponse and selection bias go uncorrected, welfare measures may be overestimated or underestimated contributing to potential errors in resource policy and management decisions. Copyright 2000, Oxford University Press.
Statistical Inference for Inequality and Poverty Measurement with Dependent Data

This article is about statistical inference for inequality and poverty measures when income data exhibit contemporaneous dependence across members of the same household. While much empirical research is based on household survey data such as the PSID, standard methods assume that income is an independent and identically distributed random variable. Applying them to contemporaneously dependent data produces biased results, and Monte Carlo experiments reveal that their confidence intervals are too narrow. By contrast, our proposed distribution-free estimators perform well.
The Wrong Shape of Insurance? What Cross-Sectional Distributions Tell Us About Models of Consumption-Smoothing

This paper shows how two standard models of consumption risk-sharing - self-insurance through borrowing and saving and limited commitment to insurance contracts - replicate similarly well the standard, second-moment measures of insurance observed in US micro-data. A non-parametric analysis, however, reveals strongly contrasting and counterfactual joint distributions of consumption, income and wealth. Method of moments estimation shows how measurement error in consumption eliminates excessive skewness and concentration of consumption growth. Moreover, counterfactual non-linearities disappear at high estimated risk-aversion under self-insurance, but are a robust feature of limited commitment. Its "shape of insurance" thus argues strongly in favour of the self-insurance model.
Modeling and Forecasting the VIX Index

This paper models the implied volatility of the S&P 500 index, with the aim of producing useful forecasts for option traders. Numerous time-series models of the VIX index are estimated, and daily out-of-sample forecasts are calculated from all relevant models. The directional accuracy of the forecasts is evaluated with market-timing tests. Option trades are simulated based on the forecasts, and their profitability is also used to rank the models. The results indicate that an ARIMA (1,1,1) model enhanced with exogenous regressors has predictive power regarding the directional change in the VIX index. GARCH terms are statistically significant, but do not improve forecasts. The best models predict the direction of change correctly for over 60 percent of the trading days. Out-of-sample option trading over a period of fifteen months yields positive returns when the forecasts from the best models are used as the basis for investment decisions.
Synthesis of a sparse linear array with multiple simultaneous frequency-invariant focused beams

As we all know, in broadband arrays situation, the beam width would narrow down with the increasing of frequency, which means the signal spectrum would be distorted in the interested frequency domain. Frequency-invariant beam pattern (FIBP) is the one whose amplitude keep almost constant in the specified range. For the superiority of the FIBP, quantities of work have been done recent years, however, almost all of these works require a large number of elements to get frequency invariant (FI) characteristics in the required mainlobe region. We now propose a new method to synthesize a frequency-invariant multi-beam pattern with space-frequency nulls, in which we can save the element number as many as possible. Besides, the pattern with multiple simultaneous FI focused beam which means radiating multiple beams at the same time, would be helpful in element reduction and system cost reduction. In order to get the desired performance, we provide multiple constraints to control the FI property and sidelobe level respectively, such as the spatial response variation (SRV) constraint, the sidelobe constraint and the look-direction constraint. We apply the iterative second-order cone programming (SOCP) along with the reweighted ℓ1-norm optimization here to simplify the problem as to minimize the effective filters in filter-and-sum structure. We carry out two examples to validate the effectiveness and advantages of the proposed method. In the first example, we construct multiple FI focused beam patterns (7-beam, 11-beam and 15 beam) and compare the results with those obtained by the methods in literature [1] and [2]. Besides good FI property and exact sidelobe control, results show that the new method performs better in element reduction. In the second example, we construct an 11-beam FI focused pattern with a space-frequency null to make the referred pattern more adaptive to real situation. The results show that not only can the element saving keep good but also can the null reach the desired level (-50 dB) at least.
Detecting low-quality crowdtesting workers

QoE crowdtesting is increasingly popular among researchers to conduct subjective assessments of different services. Experimenters can easily access to a huge pool of human subjects through crowdsourcing platforms. A fundamental problem threatening the integrity of crowdtesting is to detect cheating from the workers who work without any supervision. One of the approaches in classifying the quality of workers is analyzing their behavior during the experiments. A major challenge is to systematically analyze the mouse cursor trajectory. However, existing works usually analyze the trajectory coarsely, which cannot fully extract the information imbedded in the trajectory. In this paper, we propose to use finer-grained cursor trajectory analysis, including submovement analysis, to identify low quality workers. Our approach is to define a set of ten worker behavior metrics to quantify different types of worker behavior. A jQuery-based library was implemented to collect the worker behavior. Moreover, four different 5-point Likert scale rating methods were employed. A number of methods, including question design, instructions, and human inspections, are used to label workers into three categories. We then apply multiclass Naive Bayes classifier to construct different models using all or some of the metrics and the workers' category. Our results show that the error rates of the model trained from four metrics is equal or less than 30% for four rating methods. By combining the predictions from the four rating methods, the successful rate in detecting low-quality workers is around 80%.
The Role of Consultants in SMEs

In this article the external advice sought by small to medium-sized enterprises (SMEs) is analysed through a survey carried out to evaluate the impact of the work done by consultants and to determine the degree of satisfaction with Spanish consulting firms. We will examine the effect of management consulting firms on SMEs when an external consultant offers the consulting service. Research was carried out into companies that could potentially seek the services of an adviser by asking them if they considered the results of the adviser's project and its quality to be useful.
Is Australia's Home-ownership Rate Really Stable? An Examination of Change between 1975 and 1994

Australia has exhibited a remarkably stable home-ownership rate of 70 per cent for over thee decades. However, this paper questions whether this rate is sustainable. Decomposition techniques based on tenure choice models are used to provide evidence of changing homeownership patterns in Australia between 1975 and 1994. The results indicate a distinct change in home-ownership propensities which is neither uniform across age-groups, nor uniform for different household types with both the same and with different levels of income. A number of tentative explanations are proposed, each of which provides the basis for further research on the factors affecting household tenure decisions. This is essential if the potential for a significant decline in home-ownership is to be seen as a matter of concern.
Measuring attitudes related to interdisciplinary training : revisiting the Heinemann , Schmitt and Farrell ` attitudes toward health care teams ’ scale

Findings from an exploratory factor analysis on the 21 item `attitudes towards health care teams’ (Heinemann et al., 1999) are reported. Using data collected as part of an innovative educational program on geriatric team training program in the United States we report an exploratory factor analyses for 913 student trainees. The geriatric interdisciplinary team training (GITT) program funded by a United States philanthropic foundation, The John A. Hartford Foundation of New York City, requires medicine, nursing, and social work students to learn about geriatric teams. A 3-factor solution with all 21-items is obtained. These factors are labeled to re ̄ ect normative team constructs: team value, team ef® ciency and shared leadership. Though conceptually these factors map onto those identi® ed by Heinemann et al. (1999), some important philosophical and methodological differences are noted. Implications for interdisciplinary education and for the construct validity of this scale are discussed.
Democracy, Political Similarity, and International Alliances, 1816-1992

The connection between domestic politics and international cooperation, specifically the relationship between regime type and alliance behavior, is examined to test two central hypotheses: democracies are more likely to ally with each other, and states of any similar regime type are more likely to ally with each other. These hypotheses emerge from three theories: constructivism, economic interdependence, and credible commitments. The authors use a data set of all pairs of states from 1816 to 1992. Results show that states with similar regime type are more likely to ally with each other after 1945, although two democracies are not more likely to ally than two autocracies during this period, and distance, learning, threat, and common culture affect alliance behavior, but trade does not. Results indicate sharp limits to the connection between democracy and international cooperation.
Plasma Epstein-Barr Virus (EBV) DNA Is a Biomarker for EBV-Positive Hodgkin's Lymphoma

Purpose: Latent Epstein-Barr virus (EBV) genomes are found in the malignant cells of approximately one-third of Hodgkin's lymphoma (HL) cases. Detection and quantitation of EBV viral DNA could potentially be used as a biomarker of disease activity. Experimental Design: Initially, EBV-DNA viral load was prospectively monitored from peripheral blood mononuclear cells (PBMC) in patients with HL. Subsequently, we analyzed viral load in plasma from a second cohort of patients. A total of 58 patients with HL (31 newly diagnosed, 6 relapsed, and 21 in long-term remission) were tested. Using real-time PCR, 43 PBMC and 52 plasma samples were analyzed. Results: EBV-DNA was detectable in the plasma of all EBV-positive patients with HL prior to therapy. However, viral DNA was undetectable following therapy in responding patients (P = 0.0156), EBV-positive HL patients in long-term remission (P = 0.0011), and in all patients with EBV-negative HL (P = 0.0238). Conversely, there was no association seen for the EBV-DNA load measured from PBMC in patients with active EBV-positive HL patients as compared with EBV-negative HL, or patients in long-term remission. EBV-DNA load in matched plasma/PBMC samples were not correlated. Conclusions: We show that free plasma EBV-DNA has excellent sensitivity and specificity, and can be used as a noninvasive biomarker for EBV-positive HL and that serial monitoring could predict response to therapy. Additional prospective studies are required to further evaluate the use of free plasma EBV-DNA as a biomarker for monitoring response to treatment in patients with EBV-positive HL.
International Immigration and Economic Welfare in an Efficiency Wage Model: The Co-Existence Case of Both Legal and Illegal Foreign Workers

In the developed countries some native workers are unemployed while there exist illegal unskilled (legal skilled) foreign workers who are complementary to (substitutable for) natives and their wages are usually lower than (equal to) that of natives. Reflecting this situation, we introduce two types of immigrant in an efficiency wage model. It is shown that domestic government should exclude illegal foreign workers but welcome legal ones if the total number of illegal immigrants is small enough and well controlled. On the other hand, legal immigration should be restricted if the flood of illegal immigration is out of control. Copyright 2004 Blackwell Publishing Ltd
Cultured Nestin–Positive Cells from Postnatal Mouse Small Bowel Differentiate Ex Vivo into Neurons, Glia, and Smooth Muscle

Little is known about postnatal enteric nervous system (ENS) development, but some reports suggest that the postnatal bowel may contain neural stem cells. Therefore, we created an in vitro model of desegregation using an enzymatic and mechanical tissue technique. This approach yielded a group of cells from the small intestine of lactating and adult mice, which ex vivo attach to the culture dish; actively proliferate; and express nestin, vimentin, and the pro‐neural transcription factors neurogenin‐2 (ngn‐2), Sox‐10, and Mash‐1. In the conditions grown, double immunostains suggest that they differentiate into various cell types, particularly neurons, smooth muscle, and glia including 04 protein–positive cells. They also express the neurotrophic‐protein tyrosine kinase (Trk) receptors TrkA, TrkB, and TrkC; the low‐affinity neurotrophin receptor p75NTR; and the glial‐derived neurotrophic factor receptors (GFR)α‐1, GFRα‐2, and GFRα‐3. The neurons expressed several sensory and motor neurotransmitters present in the central and enteric nervous systems, including calcitonin gene‐related peptide, neuropeptideY, peptideYY, substance P, vasoactive intestinal polypeptide, and galanin; along with glia, these neurons formed elaborate intercellular connections. They also express c‐KIT, CD34, CD20, and CD45RO, suggesting they either have a hematogenous origin or may differentiate toward hematogenous lines. These findings suggest that these cells may be enteric neural stem cells (ENSCs); may normally be present in the small intestine; and may have the capacity to proliferate and differentiate into neurons, glia, and smooth muscle. Further identification and purification of intestinal ENSCs will provide a means to study the regulation of their differentiation and should give insight into the mechanisms involved in development and remodeling of the ENS. The possible therapeutic application of postnatal stem cells such as ENSCs needs to be evaluated, including their use for transplantation in the central nervous system.
Accelerated Parameter Mapping of Multiple-Echo Gradient-Echo Data Using Model-Based Iterative Reconstruction

A new reconstruction method, coined MIRAGE, is presented for accurate, fast, and robust parameter mapping of multiple-echo gradient-echo (MEGE) imaging, the basis sequence of novel quantitative magnetic resonance imaging techniques such as water content and susceptibility mapping. Assuming that the temporal signal can be modeled as a sum of damped complex exponentials, MIRAGE performs model-based reconstruction of undersampled data by minimizing the rank of local Hankel matrices. It further incorporates multi-channel information and spatial prior knowledge. Finally, the parameter maps are estimated using nonlinear regression. Simulations and retrospective undersampling of phantom and in vivo data affirm robustness, e.g., to strong inhomogeneity of the static magnetic field and partial volume effects. MIRAGE is compared with a state-of-the-art compressed sensing method, ${{\text {L}}_{1}}$ -ESPIRiT. Parameter maps estimated from reconstructed data using MIRAGE are shown to be accurate, with the mean absolute error reduced by up to 50% for in vivo results. The proposed method has the potential to improve the diagnostic utility of quantitative imaging techniques that rely on MEGE data.
The Value Relevance of Cash Flows, Current Accruals, and Non-Current Accruals in the UK

Cash flow statements have a longstanding history as mandated financial statement disclosures, having replaced funds flow statements. The usefulness of such disclosures with respect to one of the main purposes of financial statements—providing information relevant to the assessment of future cash flows and their uncertainty, and the market value of firms—is still subject to debate. This study investigates whether various partitions of earnings involving combinations of a cash flow measure of performance and measures of current accruals and non-current accruals improve the ability to explain market values in the UK relative to using earnings alone. Using a valuation model-based methodology, and employing a UK sample of non-financial firms for the years 1993 to 2007, our results suggest strong support for the assertion that cash flows can have incremental value relevance relative to either earnings or funds flows. By implication, cash flows can have separate value relevance from total and, in particular, current accruals. There is slightly less consistent evidence that current and non-current accruals can have separate value relevance but, nonetheless, the results are still strongly in favour in this respect. Generally, the main source of increase in explanatory power for market values is the separate inclusion of our cash flow measure in the estimated regressions. As a consequence, we conclude that the statement of cash flows in the UK provides information useful to UK investors in valuing firms. Further, requiring a cash flow statement, as opposed to a funds flow statement, improves the information content of financial statements in the UK.
Charmanteau: Character Embedding Models For Portmanteau Creation

Portmanteaus are a word formation phenomenon where two words combine into a new word. We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information. We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model. This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task. Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.
Applications of hybrid neural networks and genetic programming in financial forecasting

This thesis explores the utility of computational intelligent techniques and aims to contribute to the growing literature of hybrid neural networks and genetic programming applications in financial forecasting. The theoretical background and the description of the forecasting techniques are given in the first part of the thesis (chapters 1-3), while the contribution is provided through the last five self-contained chapters (chapters 4-8). Chapter 4 investigates the utility of the Psi Sigma neural network when applied to the task of forecasting and trading the Euro/Dollar exchange rate, while Kalman Filter estimation is tested in combining neural network forecasts. A time-varying leverage trading strategy based on volatility forecasts is also introduced. In chapter 5 three neural networks are used to forecast an exchange rate, while Kalman Filter, Genetic Programming and Support Vector Regression are implemented to provide stochastic and genetic forecast combinations. In addition, a hybrid leverage trading strategy tests if volatility forecasts and market shocks can be combined to boost the trading performance of the models. Chapter 6 presents a hybrid Genetic Algorithm – Support Vector Regression model for optimal parameter selection and feature subset combination. The model is applied to the task of forecasting and trading three euro exchange rates. The results of these chapters suggest that the stochastic and genetic neural network forecast combinations present superior forecasts and high profitability. In that way, more light is shed in the demanding issue of achieving statistical and trading efficiency in the foreign exchange markets. The focus of the next two chapters shifts from exchange rate forecasting to inflation and unemployment prediction through optimal macroeconomic variable selection. Chapter 7 focuses on forecasting the US inflation and unemployment, while chapter 8 presents the Rolling Genetic – Support Vector Regression model. The latter is applied to several forecasting exercises of inflation and unemployment of EMU members. Both chapters provide information on which set of macroeconomic indicators is found relevant to inflation and unemployment targeting on a monthly basis. The proposed models statistically outperform traditional ones. Hence, the voluminous literature, suggesting that non-linear time-varying approaches are more efficient and realistic in similar applications, is extended. From a technical point of view, these algorithms are superior to non-adaptive algorithms; avoid time consuming optimization approaches and efficiently cope with dimensionality and data-snooping issues.