Miracle-dz/UniFAR-FTUs huggingface dataset:
A NEW FRAMEWORK FOR THE ANALYSIS OF INEQUALITY

This paper presents a new framework for analyzing inequality that moves beyond the anonymity postulate. We estimate the determinants of sectoral choice and the joint distributions of outcomes across sectors. We determine which components of realized earnings variability are due to uncertainty and which components are due to components of human diversity that are forcastable by agents. Using our tools, we can determine how policies shift persons across sectors and outcome distributions across sectors.
SPMD image processing on Beowulf clusters: directives and libraries

Most image processing algorithms can be parallelized by splitting parallel loops and by using very few communication patterns. Code parallelization using MPI still involves much programming overhead. In order to reduce these overheads, we first developed a small SPMD library (SPMDlib) on top of MPI. The programmer can use the library routines themselves, because they are easy to learn and to apply, even without knowing MPI. However, in order to increase user friendliness, we also develop a small set of parallelization and communication directives/pragmas (SPMDdir), together with a parser that converts these into library calls. SPMDdir is used to develop a new version of SPMDlib. This new version contains much less but generic routines that can be optimized for different network topologies. Extensions for Fortran 90/95 and C are discussed, as well as communication optimizations.
The Digital Transformation of Healthcare: Current Status and the Road Ahead

As the United States expends extraordinary efforts towards the digitization of its healthcare system, and as policy makers across the globe look to information technology as a means of making healthcare systems safer, more affordable, and more accessible, a rare and remarkable opportunity has emerged for the information systems research community to leverage its in-depth knowledge to both advance theory and influence practice and policy. Although Health IT (HIT) has tremendous potential for improving quality and reducing cost in healthcare, significant challenges need to be overcome to fully realize this potential. In this commentary, we survey the landscape of existing studies on HIT to provide an overview of the current status of HIT research. We then identify three major areas that warrant further research: 1) HIT design, implementation, and meaningful use, 2) measurement and quantification of HIT payoff and impact, and 3) extending the traditional realm of HIT. We discuss specific research questions in each domain and suggest appropriate methods to approach them. We encourage IS scholars to become active participants in the global discourse on healthcare transformation through information technology.
Estimating leaf area index of salt marsh vegetation using airborne hyperspectral data

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute. Abstract Information on Leaf area index (LAI) can be important for an improved understanding of the ecological processes of the salt marsh ecosystem. It is very difficult to estimate LAI given the heterogeneity and large spatial gradients within salt marshes. Hyperspectral remote sensing has demonstrated wide applicability in the area of estimating and mapping LAI of forest and agricultural ecosystems. This study attempts to evaluate different empirical models in estimating the LAI of salt marsh area from a hyperspectral remote sensing data. The empirical models were derived from three different regression techniques: simple linear regression, partial least square regression and neural network. Narrow band optimal normalized difference vegetation index (optimal NDVI) and optimal modified soil adjusted vegetation index (optimal MSAVI) derived from all possible two-band combinations were used in simple linear regression. The full spectrum data (455nm-1622nm) and the useful channels selected by a genetic algorithm were used in partial least square regressions. Feature extractions for neural networks were carried out using principle component analysis and minimum noise fraction. Cross validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set (subset A, n=78) or on subsets stratified as high marsh (subset H, n=45), low marsh (subset L, n=33) and elymus vegetation type (subset E, n=15). Partial least square regression coupled with genetic algorithm provided the highest accuracy in predicting the LAI of the study area. Neural networks performed inconsistently with different subsets. Neural network provided higher accuracy than simple linear regression when entire dataset (n=78) was used in analysis. For subsets, H, L and E, simple linear regressions provided higher accuracies than neural networks. The study showed that the partial least square regression can be a useful tool for the estimation of LAI of the study area using hyperspectral data. Partial least square regression can utilize a greater number of channels of hyperspectral data than simple linear regression; whereas it was found to work well with minimum data size compared to neural network. The highest accuracy obtained for subset E emphasized the need of building vegetation type specific model to estimate LAI of the …
An energy-efficient clustering algorithm for wireless sensor networks

This paper presents an energy-efficient clustering algorithm, named EECA for short, which contains two steps: first, the node with the highest residual energy is selected as the anchor cluster-head, and the candidate cluster-heads are determined according to their residual energy as well as the distance from the anchor cluster-head; second, the candidate cluster-heads compete to be the cluster-heads via a delayed broadcast mechanism. Comparing with the well-known protocol LEACH, the two-step cluster-head selection mechanism balances the distribution of the cluster-heads. Simulations show that the proposed algorithm can prolong the network lifetime significantly.
A method for incorporating knowledge and communication into decentralized discrete-event systems

Using a formal method for reasoning about knowledge, decentralized discrete-event control problems are described. States of the system where supervisors do not have su cient knowledge to make the correct control decision can be detected. A solution where supervisors may communicate is presented. The relationship between communication and control is complex because (i) control decisions made by one supervisor may depend on communications received from another supervisor and yet (ii) the content of the information that is communicated could be a ected by information the communicating supervisor previously received. Procedures are derived for incorporating communication into decentralized discrete-event control. The resulting augmented system gives the supervisors enough knowledge to make the correct control decisions. These procedures yield a control solution while ensuring that supervisors communicate in a consistent manner. Further, these procedures yield a minimal set of communications.
Independent and Combined Effects of Maternal Prepregnancy Body Mass Index and Gestational Weight Gain on Offspring Growth at 0–3 Years of Age

Background. The objective of this study was to investigate the independent and combined effects of maternal prepregnancy body mass index (BMI) and gestational weight gain (GWG) on offspring growth at 0–3 years old. Methods. A total of 826 pairs of nondiabetic mothers and their offspring were recruited in this study. Maternal information was abstracted from medical records and questionnaires. Offspring growth trajectories of weights and BMIs were depicted based on anthropometric measurements. Results. Offspring of mothers who were prepregnancy overweight/obese or obtained excessive GWGs continuously had greater weight and BMI Z-scores throughout the first 3 years of life. Children of prepregnancy overweight/obese mothers with excessive GWGs had a phenotype of higher weight and BMI Z-scores than those prepregnancy overweight/obese ones with nonexcessive GWGs from birth to 18 months. Maternal excessive GWGs increased offspring's risk of overweight/obesity at 12 months (AOR = 1.43, 95% CI: 1.03–2.00) and 24 months (AOR = 1.51, 95% CI: 1.02–2.25). Combination of excessive prepregnancy BMIs and GWGs was significantly associated with offspring's overweight/obesity at 30 months (AOR = 2.98, 95% CI: 1.36–6.53). Conclusions. Maternal prepregnancy overweight/obesity and excessive GWG are both significantly associated with rapid offspring growth from birth to 3 years old. Excessive GWGs strengthen the effects of high maternal prepregnancy BMIs on excessive offspring growth during their early life.
Neural network based gene regulatory network reconstruction

Gene Regulatory Networks (GRN) is used to model the regulations in living organisms. Inferring genetic network from different experimental high throughput biological data (like microarray) is a challenging job for all researchers. In this paper, Artificial Neural Network, which is a very effective soft computing tool to learn and model the dynamics or dependencies between genes, is used for reconstruction of small scale GRN from the reduced microarray dataset of Lung Adenocarcinoma. The significances of regulations of one gene to other genes of the system are expressed by a weight matrix which is computed using Perceptron based biologically significant weight updating method by minimizing the error during learning. Based on the values of elements of filtered weight matrix, a directed weighted graph can be drawn successfully that denotes gene regulatory network.
Energy Consumption and GDP Causality: New Evidence from Disaggregated Data and Panel Long-Run Causality Tests of Developed and Developing Countries

This paper disaggregates energy consumption and GDP data according to end-use to analyze a broad number of developed and developing countries grouped in panels by similar characteristics. Panel long-run causality is assessed with a relatively under-utilized approach recommend by Canning and Pedroni (2008). We examine (i) reduced form production function models for both the industry and service/commercial sectors, where aggregate energy consumption is expected to cause aggregate output; and (ii) reduced form demand models, where income is expected to cause (separately) per capita residential energy consumption, per capita residential electricity consumption, and per capita gasoline consumption. We uncover for 12 different panels a set of super-consistent causality findings across three demand models that income “Granger-causes” per capita consumption. By contrast, the results from the production function models suggest that a different modeling framework is required to glean new, useful insights.
Knowledge integration for Learning-by-Reading

The knowledge acquisition bottleneck remains the fundamental obstacle to building intelligent reasoning systems. However, there is an ideal solution to this problem: creating a system that constructs knowledge bases by extracting information from texts. This approach, called Learning-by-Reading, is promising because a vast body of knowledge is already expressed in text, and – in terms of human effort – it is the least costly method of communicating information to computers. Despite this advantage, Learning-by-Reading has been infeasible due to limitations with Natural Language Understanding (NLU). In this proposal, we introduce an approach to overcome this difficulty, based on the following hypothesis: although current technologies of NLU produce imperfect semantic representations for individual texts, combining these imperfect representations drawn from multiple texts (all on the same topic) can significantly improve the overall quality of the resulting knowledge base. This approach addresses the difficulty of NLU in two ways: (1) the information drawn from one text can help reading the other texts and (2) an incorrect interpretation on one text can be repaired by the other texts with redundant content. Key in this approach is knowledge integration, the ability to coherently relate knowledge extracted from multiple texts. This paper introduces our ongoing work on a Learning-by-Reading system, Kleo, which has a sophisticated knowledge integration facility. Kleo shows that knowledge integration is effective and merits further investigation. We propose using knowledge integration to address the brittleness of key steps in natural language processing, such as parsing and semantic interpretation. First, we introduce a semantic representation language, called compact representation, which can succinctly represent the alternative interpretations of a sentence, as well as the constraints among them. Second, we present a knowledge integration algorithm that combines multiple compact representations to prune the implausible interpretations. Finally, we propose a plan for evaluating compact representations and the Kleo knowledge integration system.
State of the Art Unit Root Tests and the PPP Puzzle

Using median-unbiased estimation, recent research has questioned the validity of Rogoff’s “remarkable consensus” of 3-5 year half-lives of deviations from PPP. These estimates, however, are based on unit root tests with low power. We extend median-unbiased estimation to the efficient unit root test of Elliott, Rothenberg, and Stock (1996). We find that median-unbiased estimation based on the more powerful unit root test has the potential to tighten confidence intervals for half- lives. Using long horizon real exchange rate data, we find that the typical lower bound of the confidence intervals for median-unbiased half-lives is above 3 years. Thus, while previous confidence intervals for half-lives are consistent with virtually anything, our tighter confidence intervals now rule out economic models with nominal rigidities as candidates for explaining the observed behavior of real exchange rates. Therefore, while we obtain more information using efficient unit root tests on longer term data, this information moves us away from solving the PPP puzzle.
A fuzzy approach for measuring development of topics in patents using Latent Dirichlet Allocation

Technology progress brings the very rapid growth of patent publications, which increases the difficulty of domain experts to measure the development of various topics, handle linguistic terms used in evaluation and understand massive technological content. To overcome the limitations of keyword-ranking type of text mining result in existing research, and at the same time deal with the vagueness of linguistic terms to assist thematic evaluation, this research proposes a fuzzy set-based topic development measurement (FTDM) approach to estimate and evaluate the topics hidden in a large volume of patent claims using Latent Dirichlet Allocation. In this study, latent semantic topics are first discovered from patent corpus and measured by a temporal-weight matrix to reveal the importance of all topics in different years. For each topic, we then calculate a temporal-weight coefficient based on the matrix, which is associated with a set of linguistic terms to describe its development state over time. After choosing a suitable linguistic term set, fuzzy membership functions are created for each term. The temporal-weight coefficients are then transformed to membership vectors related to the linguistic terms, which can be used to measure the development states of all topics directly and effectively. A case study using solar cell related patents is given to show the effectiveness of the proposed FTDM approach and its applicability for estimating hidden topics and measuring their corresponding development states efficiently.
Dynamic Trajectory Generation for Serial Elastic Actuated Robots

Abstract Robotic systems can benefit from the introduction of properly chosen joint elasticity. Besides their robustness against rigid impact, the energy saving capabilities may increase the system dynamics. In this paper, a method applicable for robots with serial elastic joints is presented, which embodies a desired oscillatory behavior into the hardware and thereby leads to improved performance. This is achieved by shaping the flexible joint robot as a linear one-mode system and embodying the natural frequency of the real intrinsic behavior. An algorithm is presented for shaping the one-mode property and exciting the system via a negative definite damping term in a decoupled coordinate space. The output of the approach is a dynamic trajectory resulting in a coordinated link motion and synchronized transfer of kinetic and potential energy. Furthermore, the dynamic trajectory is commanded to the real robot via a motor PD controller, where asymptotic stability for both subsystems—i.e. the trajectory generator and the controlled robot—is proven. The method is validated on a two-link serial elastic actuated robot. Both, simulation and experiment confirm the eigenmode embodiment, energy efficiency by velocity enlargement between motor and link side motion, and synchronized joint motion.
Ocular Localization and Transduction by Adenoviral Vectors Are Serotype-Dependent and Can Be Modified by Inclusion of RGD Fiber Modifications

Purpose To evaluate localization and transgene expression from adenoviral vector of serotypes 5, 35, and 28, ± an RGD motif in the fiber following intravitreal or subretinal administration. Methods Ocular transduction by adenoviral vector serotypes ± RGD was studied in the eyes of mice receiving an intravitreous or subretinal injection. Each serotype expressed a CMV-GFP expression cassette and histological sections of eyes were examined. Transgene expression levels were examined using luciferase (Luc) regulated by the CMV promoter. Results GFP localization studies revealed that serotypes 5 and 28 given intravitreously transduced corneal endothelial, trabecular, and iris cells. Intravitreous delivery of the unmodified Ad35 serotype transduced only trabecular meshwork cells, but, the modification of the RGD motif into the fiber of the Ad35 viral vector base expanded transduction to corneal endothelial and iris cells. Incorporation of the RGD motif into the fiber knob with deletion of RGD from the penton base did not affect the transduction ability of the Ad5 vector base. Subretinal studies showed that RGD in the Ad5 knob shifted transduction from RPE cells to photoreceptor cells. Using a CMV-Luc expression cassette, intravitreous delivery of all the tested vectors, such as Ad5-, Ad35- and Ad28- resulted in an initial rapid induction of luciferase activity that thereafter declined. Subretinal administration of vectors showed a marked difference in transgene activity. Ad35-Luc gene expression peaked at 7 days and remained elevated for 6 months. Ad28-Luc expression was high after 1 day and remained sustained for one month. Conclusions Different adenoviral vector serotypes ± modifications transduce different cells within the eye. Transgene expression can be brief or extended and is serotype and delivery route dependent. Thus, adenoviral vectors provide a versatile platform for the delivery of therapeutic agents for ocular diseases.
ConfMapper: Automated Variable Finding for Configuration Items in Source Code

Misconfigurations have become the major cause of software failures as a consequence of software development. Traditional methodology of misconfiguration diagnosis based on program analysis has distinct advantages in accuracy and efficiency. However, the state-of-the-art researches do the mapping between the configuration items and relevant program variables either manually locating or following specified annotation with fixed format, which requires enormous domain knowledge and heavy workload. In order to address the challenges, we manually studied eight popular open-source software and observed some mapping-related features. Based on these observations, we proposed a tool named ConfMapper to accomplish the automated mapping from the configuration items to the relevant program variables without understanding the complicated semantic context in source code. We carried out comprehensive experiments upon seven popular open-source software. ConfMapper could reach nearly 100% accuracy in mapping the configuration items to program variables, and mine about 91.5% potential configuration items in average.
Short Sales and Fundamental Value: Explaining the REIT Premium to NAV

This study explores the role of short sale constraints in explaining the variation in premiums to Net Asset Value (NAV) in REIT pricing. We use proprietary information on short sales between June 2006 and September 2008 to examine how short sales and short sale constraints affect the variation in monthly REIT NAV premiums using panel vector autoregression models. We find that variation in short sale activity across individual REITs can account for at least one-third of the variation in NAV premiums. Short sale constraints tend to be binding when there is strong demand and limited supply of shares to short. Excess demand leads to overvaluation and the correction of the overvaluation explains the under-performance of premium REITs.
New Public Management Comes to America

The United States has had an ambiguous relationship to the New Public Management (NPM) as a global public sector reform movement. Many of the themes, concepts, and tools of governance associated with NPM's managerial ideology-"business-like management," competitive tendering, incentives, agencification, devolution, performance measurement and management, subsidiarity- have long been staples of U.S. federal, state and local public management. Most of the elements of NPM have been road-tested in America. Yet public management reform in the U.S. in the NPM era that was underway in the 1980s has been decidedly heterodox, a manifestation of what Paul Light (1997) has termed the variously-themed "tides of reform" that ebb and flow through American public administration. Beginning in 2001, with the administration of George W. Bush, a new tide of reform has surged in, displacing the Clinton administration's emphasis on "reinventing government. " The Bush reforms have attracted surprisingly little attention compared to the swarms of public management experts who examined the Clinton initiatives from their outset. Whereas Lyndon Johnson sought to use systematic analysis to strengthen executive control over policy and budget priorities, Richard Nixon sought to create a centralized "administrative presidency," Jimmy Carter attempted to enhance executive control over the assignment and remuneration of senior civil servants, Ronald Reagan proclaimed a "war on waste" and inefficiency and Bill Clinton promised to "let managers manage" toward a government that "works better and costs less," George W. Bush has undertaken virtually all of these things to at least some degree-and more. Despite their apparent breadth, however, Bush reforms have reflected more of the essential spirit of New Public Management than those of any other recent American president.