Miracle-dz/UniFAR-FTUs huggingface dataset:
Using data to inform policy

In this paper, a general framework is proposed for the use of (quasi-) experimental data when choosing policies such as tax rates or the level of inputs in a production process. The data are used to update expectations about social welfare as a function of policy, and the policy is chosen to maximize expected social welfare. We characterize the properties of the implied decision function. For settings where experimentation is feasible, we characterize experimental designs maximizing expected social welfare, assuming policies are chosen based on the data. We discuss several economic settings which are covered by our framework, and apply our methods to data from the RAND health insurance experiment. In this application, we obtain much smaller estimates of the optimal copay (18% vs. 50%) than those obtained using a conventional sucient-statisti c approach. Our approach combines optimal policy theory, statistical decision theory, and nonparametric Bayesian methods. It explicitly takes into account economic theory, does not rely on restrictions of functional form or heterogeneity, and provides a transparent mapping from observations to policy choices. This mapping is optimal in nite samples.
Sources of Agricultural Growth in India: Role of Diversification towards High-Value Crops

"This study examines the sources of crop income growth in Indian agriculture over the 1980s and 1990s. Using a method developed by Minot (2003), the analysis decomposes crop income growth into the contribution of yield increases, area expansion, price increases, and diversification from low-value crops to higher-value crops. The results confirm that at the national level, technology (higher yield) was the main source of crop income growth during 1980s, while rising prices and diversification emerged as the dominant sources of growth in agriculture during 1990s. Diversification towards higher-value crops such as fruits and vegetables accounted for about 27% of crop income growth in the 1980s and 31% in the 1990s. However, these national averages hide substantial regional variation. In the grain-dominated northern and eastern regions, price increases were the most important source of growth during 1990s, while in the southern and western regions crop income growth was led by diversification into higher-value crops. The results reflect the slowing growth of wheat and rice yields in India, as well as the growing importance of diversification into higher-value crops. Restoring the growth in grain yields will require investment in agricultural research and development, while facilitating further diversification involves institutional development to better link small farmers with growing markets for high-value commodities. " Author's Abstract
Optimization of Distributed Processors for Power System: Kalman Filters using Petri Net

The growth and interconnection of power networks in many regions has invited complicated techniques for energy management services (EMS). State estimation techniques become a powerful tool in power system control centers, and that more information is required to achieve the objective of EMS. For the online state estimator, assuming the continuous time is equidistantly sampled with period processing events must be finished within this period. Advantage of Kalman Filtering (KF) algorithm in using system information to improve the estimation precision is utilized. Computational power is a major issue responsible for the achievement of the objective, i.e. estimators’ solution at a small sampled period. This paper presents the optimum utilization of processors in a state estimator based on KF. The model used is presented using Petri net (PN) theory. Keywords— Kalman filters, model, Petri Net, power system, sequential State estimator.
Statistical Issues in Assessing Forensic Evidence

In 2009, the National Academy of Sciences released a report that emphasized the need for more scientific research in the evaluation of methods in the forensic science disciplines. This needed research includes statistical issues such as bias quantification, validation and estimates of accuracy and precision in different contexts. This article reviews statistical aspects of forensic science with particular reference to the design of studies that are essential to evaluate inferences from forensic evidence. Many sources can affect both the accuracy and the consistency of decisions at each stage of the process, from specimen collection to final decision. The article discusses the importance of identifying these sources and the statistical principles involved in the quantification of, and uncertainty in, estimated probabilities of error. By contrasting the design of a successful community clinical trial with two previous fingerprint studies and with bullet lead studies, this article emphasizes the need for reduced subjectivity, the types of measurements on physical evidence that can lead to more accurate and consistent decisions and the importance of carefully designed studies in the evaluation of forensic evidence.
Full-Frame Video Stabilization via SIFT Feature Matching

Removal of visually unpleasant motion from videos is an important video enhancement technology. We present a feature-based approach for video stabilization that produces stabilized videos, while preserving the original resolution. To characterize the global/camera motion, SIFT features are extracted and carefully chosen to define the homography of 2D perspective warp between two consecutive frames. To remove high-frequency components from the original motion path, the time-domain filter is applied to generate the motion-compensated version of transformation associated with each frame. Then, a missing pixel is filled in by the average of the warped pixels. The experimental results have shown that the proposed method is effective in full-frame video stabilization.
Rosuvastatin in the primary prevention of cardiovascular disease among patients with low levels of low-density lipoprotein cholesterol and elevated high-sensitivity C-reactive protein: rationale and design of the JUPITER trial.

Completed randomized trials of statin therapy demonstrate that 3-hydroxy-3-methylglutaryl–coenzyme A (HMG-CoA) reductase inhibitors reduce the risk of myocardial infarction, stroke, and other cardiovascular events among individuals with established coronary disease and overt hyperlipidemia.1–6 In aggregate, use of statin therapy in these trials has been associated with an approximate 30% reduction in cardiovascular event rates. Largely on the basis of these cholesterol reduction trials, current treatment algorithms from the National Cholesterol Education Program (NCEP) Adult Treatment Panel III endorse the use of statins in secondary prevention and encourage increased use of statins in primary prevention among those with hyperlipidemia and diabetes.7 Unfortunately, despite evidence provided by the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS2) and the West of Scotland Coronary Prevention Study (WOSCOPS3), use of statins for the primary prevention of cardiovascular disease has not been widely adopted in a cost-effective manner. From a clinical perspective, there are several reasons for this slow adoption. First, almost half of all cardiovascular events occur among apparently healthy men and women who have normal or even low levels of LDL cholesterol (LDL-C). Thus, better screening methods are needed in primary prevention to detect high-risk individuals for whom the number needed to treat (NNT) is small enough to make prophylactic statin therapy cost effective. Second, there has been controversy within the completed clinical trials suggesting that the benefits of statins may extend beyond LDL-C reduction alone. In both the Heart Protection Study of stable high-risk patients6 and the MIRACL (Myocardial Ischemia Reduction with Aggressive Cholesterol Lowering) study of patients with acute coronary syndromes,8 the risk reduction associated with statin therapy was almost identical among those with low as well those with as high levels of LDL-C. Further, statin therapy reduces the risk of stroke, yet LDL-C is not an …
Similarity Evaluation of English Translations of the Holy Quran

In this paper, similarity evaluation of English translations of the Holy Quran using four computational methods is performed. The computational methods are bag of words, term-frequency, tfidf and latent semantic indexing. We used twenty-one translation pairs from seven English translations (Hilali, Yusuf Ali, Sahih, Shakir, Arberry, Pickthall, Maududi) in our experiments. The similarity measures were evaluated pair wise. Based on our results, seven translations pair have high similarity measures, (Hilali, Yusuf Ali), (Hilali, Sahih), (Sahih, Shakir), (Hilali, Pickthall), (Pickthall, Shakir), (Hilali, Shakir), (Shakir, Arberry). We have nine translation pairs with low similarity measures, (Hilali, Maududi), (Maududi, Pick-thall), (Maududi, Yusuf Ali), (Hilali, Arberry), (Arberry, Yusuf Ali), (Maududi, Arberry), (Pickthall, Yusuf Ali), (Shahih, Yusuf Ali), (Pickthall, Sahih). These results from a computational perspective offer new insights into the similarity evaluation between the English translations based on computation methods. Also, we concluded the translations of (Hilali, Sahih, Shakir, Yusuf Ali, Pickthall) could be clustered into one group, and the translations of (Maududi, Arberry) into another group. These results could be used for classification of English translations and for comparing future translations.
DISCRIMINATION AND BIOPHYSICAL CHARACTERIZATION OF CERRADO PHYSIOGNOMIES WITH EO-1 HYPERSPECTRAL HYPERION

The Brazilian savanna, locally known as "cerrado", is the most intensely stressed biome with both naturaland human-induced pressures. In this study, we aimed to improve discrimination and characterization of the Brazilian cerrado physiognomies using hyperspectral Hyperion imagery, the first space-borne imaging spectrometer onboard the NASA's Earth Observing-1 (EO-1) platform. A Hyperion image was acquired over the Brasilia National Park (BNP) on July 20, 2001. Various optical measures which took a full advantage of hyperspectral remote sensing were employed and applied to the atmospherically-corrected Hyperion data. These included the 1st-order derivative-based green vegetation index (1st-DGVI), ligno-cellulose absorption index, spectral albedo, and shortwave-infrared (SWIR) spectral unmixing. All these optical measures were correlated well with field-estimates of landscape component cover fractions. Especially, the SWIR spectral unmixing results simultaneously biophysically-characterized and discriminated the cerrado physiognomies. These preliminary analyses showed a great potential of hyperspectral remote sensing in characterizing/discriminating the land covers in the Brazilian cerrado.
Offline replication and online energy management for hard real-time multicore systems

For real-time embedded systems, energy consumption and reliability are two major design concerns. We consider the problem of minimizing the energy consumption of a set of periodic real-time applications when running on a multi-core system while satisfying given reliability targets. Multi-core platforms provide a good capability for task replication in order to achieve given reliability targets. However, careless task replication may lead to significant energy overhead. Therefore, to provide a given reliability level with a reduced energy overhead, the level of replication and also the voltage and frequency assigned to each task should be determined cautiously. The goal of this paper is to find the level of replication, voltage and frequency assignment, and core allocation for each task at design time, in order to achieve a given reliability level while minimizing the energy consumption. Also, at run-time, we find the tasks that have finished correctly and cancel the execution of their replicas to achieve even more energy saving. We evaluated the effectiveness of our scheme through extensive simulations. The results show that our scheme provides up to 50% (in average by 47%) energy saving while satisfying a broad range of reliability targets.
What Drives Mobile Commerce? An Antecedent Model of Mobile Commerce Adoption

Mobile commerce - the consumer’s engagement in online transactions with sellers using mobile devices - differs from traditional or electronic commerce due to the potential for location-specific real-time transactions and the unique attributes of mobile devices. This paper aims to understand what drives consumers to engage in mobile transactions by viewing m-commerce adoption as a process consisting of three distinct, yet inter-related behaviors: (a) ‘getting information’ (b) ‘giving information,’ and (c) ‘purchasing’ products and services using mobile devices. First, these three behaviors are integrated, using the theory of implementation intentions. Second, following the theory of planned behavior, each behavioral intention is predicted through its attitude, subjective norm, and perceived behavioral control (self-efficacy and controllability). Third, a set of beliefs for each of the three m-commerce behaviors is identified, resulting in a comprehensive model of the drivers of m-commerce adoption. Finally, an empirical study with consumers in the United States tests and validates the proposed m-commerce adoption model. Implications for the adoption of m-commerce are proposed.
Analysis of Cellular Automata Diffusion Models in Marketing

Diffusion of new products is an important problem in marketing research. One of the most prominent models in diffusion theory is the Bass Diffusion model that describes the number of adopters of new products as a differential equation. More recently, diffusion of products has been studied using agent-based modeling and in particular, stochastic cellular automata models. In this study we explore the relation between the Bass model and the cellular automata models. In particular, we explore the differences between the two models that is due to the fact that time is continuous in the Bass model but discrete in cellular automata models, and describe the conditions under which the cellular automata models converge to the Bass model. Empirical verifications of the main results are also discussed.
Enabling the International Criminal Court to Punish Aggression

According to Article Five of the Rome Statute, the International Criminal Court (ICC) cannot exercise its jurisdiction over the crime of aggression until amendments are adopted: (1) defining the crime; and (2) setting out the conditions, consistent with the United Nations (U.N.) Charter, under which the ICC is to act. This Essay analyzes the problems posed by these stipulated requirements, and suggests solutions to fulfill them.
Molecular risk stratification in advanced heart failure patients

Risk stratification in advanced heart failure (HF) is crucial for the individualization of therapeutic strategy, in particular for heart transplantation and ventricular assist device implantation. We tested the hypothesis that cardiac gene expression profiling can distinguish between HF patients with different disease severity. We obtained tissue samples from both left (LV) and right (RV) ventricle of explanted hearts of 44 patients undergoing cardiac transplantation or ventricular assist device placement. Gene expression profiles were obtained using an in‐house microarray containing 4217 muscular organ‐relevant genes. Based on their clinical status, patients were classified into three HF‐severity groups: deteriorating (n= 12), intermediate (n= 19) and stable (n= 13). Two‐class statistical analysis of gene expression profiles of deteriorating and stable patients identified a 170‐gene and a 129‐gene predictor for LV and RV samples, respectively. The LV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 88% and 92%, and a specificity of 100% and 96%, respectively. The RV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 100% and 96%, and a specificity of 100% and 100%, respectively. The molecular prediction was reproducible across biological replicates in LV and RV samples. Gene expression profiling has the potential to reproducibly detect HF patients with highest HF severity with high sensitivity and specificity. In addition, not only LV but also RV samples could be used for molecular risk stratification with similar predictive power.
Lanthanide-binding helix-turn-helix peptides: Solution structure of a designed metallonuclease

A designed lanthanide-binding chimeric peptide based on the strikingly similar geometries of the EF-hand and helix-turn-helix (HTH) motifs was investigated by NMR and CD spectroscopy and found to retain the same overall solution structure of the parental motifs. CD spectroscopy showed that the 33-mer peptide P3W folds on binding lanthanides, with an increase in α-helicity from 20% in the absence of metal to 38% and 35% in the presence of excess Eu(III) and La(III) ions, respectively. The conditional binding affinities of P3W for La(III) (5.9 ± 0.3 μM) and for Eu(III) (6.2 ± 0.3 μM) (pH 7.8, 5 mM Tris) were determined by tryptophan fluorescence titration. The La(III) complex of peptide P3, which differs from P3W by only one Trp-to-His substitution, has much less signal dispersion in the proton NMR spectra than LaP3W, indicating that the Trp residue is a critical hydrophobic anchor for maintaining a well-folded helix-turn-helix structure. A chemical-shift index analysis indicates the metallopeptide has a helix-loop-helix secondary structure. A structure calculated by using nuclear Overhauser effect and other NMR constraints reveals that P3W not only has a tightly folded metal-binding loop but also retains the α−α corner supersecondary structure of the parental motifs. Although the solution structure is undefined at both the N and C termini, the NMR structure confirms the successful incorporation of a metal-binding loop into a HTH sequence.
GENERALIZED CHAIN RATIO REGRESSION AND CHAIN REGRESSION ESTIMATORS FOR THE POPULATION MEAN USING TWO AUXILIARY CHARACTERS IN THE PRESENCE OF NON-RESPONSE

Generalized chain ratio regression and chain regression estimators for the population mean using two auxiliary characters have been proposed in the presence of non-response. The expressions for the mean square error (MSE) of these estimators have been obtained for fixed sample sizes ( n′ and n ) and also for fixed cost 0 C C ≤ . The optimum values of the first phase sample size n′ and the sub sample of size n have been obtained for the fixed cost 0 C C ≤ and also for the specified precision V = 0 V ′ .
How to Overcome the Slow Death of Intercropping in the North China Plain

Intercropping has a strong potential to counteract the severe degradation of arable land in the North China Plain (NCP). However, a rapid decline of intercropping can be observed in the last decades. The present paper investigates the reason for this development and suggests solutions on how to adjust intercropping systems to fit modern agriculture. Firstly, the developments of socioeconomic conditions for farming were assessed, analyzing the statistical yearbooks of the seven provinces of the North China Plain. Secondly, a survey was conducted in the study region to understand the current state and future of intercropping systems. The investigations revealed that, due to limited off-farm income possibilities in the past, intercropping has been a viable solution to intensively use the limited land resources per farm household. However, a shift of rural laborers into other sectors has recently been observed. Thus, decreasing importance of income from agriculture and increasing labor costs are heralding the slow death of labor-intensive intercropping systems. Two possible solutions are discussed in the paper. Either the traditional row-intercropping systems can be transformed into strip-intercropping systems that can be mechanized using existing machinery; or, new machinery has to be developed that enables the mechanization of the traditional row-intercropping systems.
Research on Female Neck Rhizosphere Line of Virtual Mannequin Based on Point-Cloud Data

With the rapid development of computer network and information technology, traditional clothing industry has taken a giant stride forward to computer information and digitization. In this paper, 250 3D point-cloud photos of young females were selected as subjects, and related characteristic points of neck rhizosphere (including front neck point, side neck point and back neck point) were determined. Then the height size, width size, thickness size and girth size of characteristic points were measured by the software named Imageware 12.1. At last, with software called Excel and SPSS, the height rules of characteristic points were analyzed, front and back neck rhizosphere were obtained by width and thickness sizes of characteristic points. The research in this paper has laid the foundation for building female neck rhizosphere line of virtual mannequin which provides datum line and basic sizes for 3D collar patterns
How to Disappear Completely: A Survey of Private Peer-to-Peer Networks

This paper offers a survey of the emerging field of private peer-to-peer networks, which can be defined as internet overlays in which the resources and infrastructure are provided by the users, and new users may only join by personal invitation. The last few years have seen rapid developments in this field, many of which have not previously been described in the research literature. We describe deployed systems, classify them architecturally, and identify some technical and social tradeoffs in the design of private peer-to-peer networks.
Monte Carlo Semantics: McPIET at RTE4

The Monte Carlo Pseudo Inference Engine for Text (MCPIET) addresses the RTE problem within a new theoretic framework for robust inference and logical pattern processing based on integrated deep and shallow semantics. In this report we outline, in some detail, this new theoretic framework, and we will use it to shed some light on the informativity and robustness characteristics for the extreme cases of deep and shallow processing. Unsurprisingly, it will turn out that there is a tradeoff between informativity and robustness. We will be able to characterize an important new notion of a degree of validity, and provide some evidence to suggest that this concept plays a crucial role in the robustness of shallow inference. At the same time our framework still supports informationally rich semantic representations and background theories, which play the central role in the informativity of deep inference. Within our new theory we can then pose, from a completely new perspective, the problem of deep/shallow integration, and also propose a solution to it, which we will call Monte Carlo Semantics.
Precise average redundancy of an idealized arithmetic coding

Redundancy is defined as the excess of the code length over the optimal (ideal) code length. We study the average redundancy of an idealized arithmetic coding (for memoryless sources with unknown distributions) in which the Krichevsky and Trofimov (1981) estimator is followed by the Shannon-Fano code. We shall ignore here important practical implementation issues such as finite precisions and finite buffer sizes. In fact, our idealized arithmetic code can be viewed as an adaptive infinite precision implementation of arithmetic encoder that resembles Elias coding. However, we provide very precise results for the average redundancy that takes into account integer-length constraints. These findings are obtained by analytic methods of analysis of algorithms such as theory of distribution of sequences modulo 1 and Fourier series. These estimates can be used to study the average redundancy of codes for tree sources, and ultimately the context-tree weighting algorithms.