Miracle-dz/UniFAR-FTUs huggingface dataset:
Health system zeitgeist: How tweets can provide real-time insight into the health system

Members of the health care workforce and health care practitioner organizations are amongst those who make use of micro-blogging services such as Twitter, to share news and updates. In this paper we propose a system to gauge across the health system and in particular in relation to the practitioners whom make use of this media system, based upon URLs tweeted and shared, what are the current topics of interest and perspectives. In this way, the proposed system enables new abilities for gathering insights into the real-time, system-wide status of a health industry, for example, the most frequently occurring themes or the most frequently health-related URLs shared by the health systems workforce.
The Assignment of Scores Procedure for Ordinal Categorical Data

Ordinal data are the most frequently encountered type of data in the social sciences. Many statistical methods can be used to process such data. One common method is to assign scores to the data, convert them into interval data, and further perform statistical analysis. There are several authors who have recently developed assigning score methods to assign scores to ordered categorical data. This paper proposes an approach that defines an assigning score system for an ordinal categorical variable based on underlying continuous latent distribution with interpretation by using three case study examples. The results show that the proposed score system is well for skewed ordinal categorical data.
The Open Method of Coordination (OMC)

We interpret the Open Method of Coordination (OMC), recently adopted by the EU as a mode of governance in the area of social policy and other ¯elds, as an imitative learning dynamics of the type considered in evolutionary game theory. The best-practise feature and the iterative design of the OMC correspond to the behavioral rule \imitate the best. " In a redistribution game with utilitarian gov- ernments and mobile welfare bene¯ciaries, we compare the outcomes of imitative behavior (long-run evolutionary equilibrium), decentralized best-response behavior (Nash equilibrium), and coordinated policies. The main result is that the OMC allows policy coordination on a strict subset of the set of Nash equilibria, favoring in particular coordination on intermediate values of the policy instrument.
Linear Precoding Based on Sub-Channel Permutation in Post-Combining MIMO-HARQ Systems

A powerful HARQ-based linear precoding scheme is proposed to utilize the flexibility of post-combining HARQ strategy in MIMO communications systems. The scheme selects the appropriate symbols and transmit powers for each eigen-mode to acquire more performance gains. Simulation results show that the proposed scheme achieves about 5.5dB signal-to-noise ratio gains over original spatial multiplexing scheme at an average bit error rate of 10 -4 . Furthermore, the gap between the two schemes increases with the number of transmissions.
Multivariate regression for pivot selection: A preliminary study

Pivot selection partially dominates the query performance of pivot-based metric space indexing methods. Lacking of coordinate system in metric spaces, mathematical tools for Rn had been prevented from being applied to metric spaces. The pivot space model embeds a metric space into a high dimensional space while preserving all the pair-wise distances. As a result, pivot selection turns into a special form of dimension reduction which only selects existing dimensions. Given a set of predictors and a response variable, multivariate regression finds a subset of predictors to predict the response variable. Considering dimensions as predictors and artificially specifying the response variable, we show how multivariate regression methods can be applied to pivot selection. Empirical results show that regression methods have comparable query performance with the commonly used corner selection heuristic, although not as good as the PCA-based method. As a preliminary study, this paper demonstrates a new possible research direction of pivot selection.
Game Theoretical Computation Based Energy Efficient Routing for Wireless Sensor Networks

The paper aims to provide an energy efficient routing algorithm for Wireless Sensor Networks (WSNs). Clustering is a technique widely adopted in WSNs. It divides the network into clusters and the cluster heads are responsible for forwarding the aggregated data to the sink. There are many algorithms for forwarding the data to the sink. In this paper, a Game theory based Energy efficient Routing Algorithm (GERA) is proposed. As a clustering algorithm GERA adopts game theory and set a utility function based on the benefit and energy consumption of the node. Cluster heads are selected based on the utility function of the node iteratively. The cluster formation is done based on the cost function of the node in the network. Also an intra-cluster and multi-hop inter-cluster routing algorithms are designed and the simulation results shows that cluster heads are evenly distributed. The proposed routing algorithm consumes less energy than other existing algorithms such as Low Energy Adaptive Clustering Hierarchy (LEACH) and Density-based Energy-efficient Game-theoretic Routing Algorithm (DEGRA). Also the network lifetime is largely prolonged.
Efficient and stable solution of structured Hessenberg linear systems arising from difference equations

This paper is concerned with the efficient solution of (block) Hessenberg linear systems whose coefficient matrix is a Toeplitz matrix in (block) Hessenberg form plus a band matrix. Such problems arise, for instance, when we apply a computational scheme based on the use of difference equations for the computation of many significant special functions and quantities occurring in engineering and physics. We present a divide-and-conquer algorithm that combines some recent techniques for the numerical treatment of structured Hessenberg linear systems. Our approach is computationally efficient and, moreover, in many practical cases it can be shown to be componentwise stable. Copyright © 2000 John Wiley & Sons, Ltd.
A Video Mosaic Algorithm for Compressed Video

A video mosaic method is proposed in this paper. Firstly, phase correlation is used to roughly compute translation offset between the first corresponding frames of two video streams, which speeds up corner match procedure and improves matching stability as well. Secondly, SIFT method is used to detect and match corners in the overlapped region. Then, a RANSAC technique is applied to eliminate outliers to ensure effectiveness of the matched corner pairs. Singular value decomposition-least square(SVDLS) method and Levenberg-Marquardt optimization are used to robustly determine the 8-parameters transform. For the other frames left, we calculate a global motion vector between consecutive frames from motion vectors included in the compressed video data, thereafter obtain projection matrix between two frames from that of the previous frame together with the global motion of each input video sequence. In the last step of the algorithm, a multi-band blending technique is used to generate the final panorama. Invalid parameters are verified by the translation offset to make Levenberg-Marquardt optimization more successful. The experiments show that the proposed algorithm exceeds existing ones at matching speed and stability.
On the Robustness and Prospects of Adaptive BDDC Methods for Finite Element Discretizations of Elliptic PDEs with High-Contrast Coefficients

Balancing Domain Decomposition by Constraints (BDDC) methods have proven to be powerful preconditioners for large and sparse linear systems arising from the finite element discretization of elliptic PDEs. Condition number bounds can be theoretically established that are independent of the number of subdomains of the decomposition. The core of the methods resides in the design of a larger and partially discontinuous finite element space that allows for fast application of the preconditioner, where Cholesky factorizations of the subdomain finite element problems are additively combined with a coarse, global solver. Multilevel and highly-scalable algorithms can be obtained by replacing the coarse Cholesky solver with a coarse BDDC preconditioner. BDDC methods have the remarkable ability to control the condition number, since the coarse space of the preconditioner can be adaptively enriched at the cost of solving local eigenproblems. The proper identification of these eigenproblems extends the robustness of the methods to any heterogeneity in the distribution of the coefficients of the PDEs, not only when the coefficients jumps align with the subdomain boundaries or when the high contrast regions are confined to lie in the interior of the subdomains. The specific adaptive technique considered in this paper does not depend upon any interaction of discretization and partition; it relies purely on algebraic operations. Coarse space adaptation in BDDC methods has attractive algorithmic properties, since the technique enhances the concurrency and the arithmetic intensity of the preconditioning step of the sparse implicit solver with the aim of controlling the number of iterations of the Krylov method in a black-box fashion, thus reducing the number of global synchronization steps and matrix vector multiplications needed by the iterative solver; data movement and memory bound kernels in the solve phase can be thus limited at the expense of extra local flops during the setup of the preconditioner. This paper presents an exposition of the BDDC algorithm that identifies the current computational bottlenecks that could prevent it from being competitive with other solvers, and proposes solutions in anticipation of exascale architectures. Furthermore, the discussion aims to give interested practitioners sufficient insights to decide whether or not to pursue BDDC in their applications. In addition, the work presents novel numerical results using the distributed memory implementation of BDDC in the PETSc library for vector field problems arising in the context of porous media flows and electromagnetic modeling; the results provide evidence of the robustness of these methods for highly heterogenous problems and non-conforming discretizations.
An adaptive energy-aware routing protocol for MANETs using the SARSA reinforcement learning algorithm

In MANETs (Mobile Ad-hoc NETworks), communicating nodes are powered by batteries which could not be re-charged in many practical usage scenarios. Hence, maximizing network lifetime is a critical optimization objective in routing protocols design for MANETs. To meet this objective, energy-consumption should be balanced among all mobile nodes. In this paper, we formulate the energy-aware route discovery problem in a reactive routing protocol as a Reinforcement Learning (RL) problem that we solve using the SARSA RL algorithm. We have implemented our proposed RL-model on the top of AODV a well-known reactive routing protocol for MANETs. Furthermore, we show through simulations the efficiency of our proposal, against an implementation of the Energy-Aware Probability routing protocol.
Time Series Evidence on the Employment Effect of Minimum Wages in Brazil

The impact of minimum wages on the labour market has been a recurrent issue in the theoretical and empirical literature [e.g., Cubitt and Heap (1999), Dickens et al. (1998), Waltman et al. (1998), and Dolado et al. (1996)]. There are two clear and divergent views on the normative impact of minimum wages on labour markets in developing economies. The advocate view holds that minimum wages redistribute resources in a welfare enhancing way, and as such have the potential to reduce poverty, enhance productivity, and foster economic growth. The distortionist view suggests that minimum wage interventions misallocate labour, waste resources through rent-seeking, impair adjustment to economic shocks, deter investment and reduce growth rates with the effect of depressing wages where most of the poor are found - i.e., in the urban informal sector and in the rural sector [Freeman (1994)]. In this paper, we present estimates on the impact of the minimum wage on the Brazilian labour markets using time series data for the period between 1982 to 1999. We undertake this task by means of cointegration analysis based on methods recently proposed by Inder (1995) and Johansen (1988), and tests of super-exogeneity for the minimum wage [Ericsson and Irons (1995)].
Two-stage computational bio-network discovery approach for metabolites: Ovarian cancer as a case study

Machine learning and other computational techniques have been applied in identifying biomarkers and constructing computational predictive models for early diagnosis of ovarian cancer. Most studies focus on large biopolymers such as DNA, RNA and Proteins but small metabolic molecules have received significantly less attention. In addition, studies have focused only on the analysis of classification performance of the biomarkers selected by various feature selection methods but do not consider possible temporal relationship among feature subsets. In this paper, we propose a two-stage bio-network discovery approach for ovarian cancer metabolites. At the first stage, feature selection is carried out using four different selection methods. The best features are selected based on overall best classification performance. At the second stage, Dynamic Bayesian Network (DBN) is used to model the temporal relationship among the stratified features. The results show that 39 features out of a total of 592 metabolomics features selected by the Least Angle Shrinkage and Selection Operator (LASSO) feature selection method yielded the highest predictive accuracy of 93%. Two DBN methods are then used to model the temporal relationships among the 39 features. The results show consistently significant relationships between the cancer biomarkers at features 219 and 225, and between features 543 and 219 across time points. Among these metabolites, around 20 metabolic chemical compounds have been identified, which could be regarded as potential biomarkers associated with ovarian cancer.
Identifying IT Purchases Anomalies in the Brazilian Government Procurement System Using Deep Learning

The Department of Research and Strategic Information (DIE), from the Brazilian Office of the Comptroller General (CGU), is responsible for investigating potential problems related to federal expenditures. To pursue this goal, DIE regularly has to analyze large volumes of data to search for anomalies that can reveal suspicious activities. With the growing demand from the citizens for transparency and corruption prevention, DIE is constantly looking for new methods to automate these processes. In this work, we investigate IT purchases anomalies in the Federal Government Procurement System by using a deep learning algorithm to generate a predictive model. This model will be used to prioritize actions carried out by the office in its pursuit of problems related to this kind of purchases. The data mining process followed the CRISP-DM methodology and the modeling phase tested the parallel resources of the H2O tool. We evaluated the performance of twelve deep learning with auto-encoder models, each one generated under a different set of parameters, in order to find the best input data reconstruction model. The best model achieved a mean squared error (MSE) of 0.0012775 and was used to predict the anomalies over the test file samples.
Global and Euro Imbalances: China and Germany

We analyse global and euro area imbalances by focusing on China and Germany as large surplus and creditor countries. In the 2000s, domestic reforms in both countries expanded the effective labour force, restrained wages, shifted income towards profits and increased corporate saving. As a result, both economies' current account surpluses widened before the global financial crisis, and that of Germany has proven more persistent as domestic investment has remained subdued. (This abstract was borrowed from another version of this item.)
Unbiased Learning for Hierarchical Models

It is known that overfitting occurs when a conventional statistical learning method such as maximum likelihood estimation, maximum a posteriori estimation or Bayesian estimation is applied to hierarchical models. This paper gives an explanation why overfitting occurs and propose an appropriate learning framework unbiased learning for hierarchical models. The method suggest to train the hyperparameters based on unbiased likelihood which is estimated by an appropriate information criterion. Therefore, it can say that the unbiased learning is a generalization of hyperparameters selection. Unbiased learning with several information criteria is tested by computer simulations.
undersTanding undergraduaTes: WhaT does PhenoMenograPhy Tell us abouT learners

Over the last three decades, librarians have championed information literacy to their institutions in a variety of ways, often taking on new roles as teachers, programmers, and departmental consultants. In their efforts to understand the best course of action to recommend for information literacy programming, librarians scrutinize information from a variety of sources, e.g., scholarly articles, books, listservs, conferences sessions, colleagues, etc. Sometimes however, the most valuable information is derived from our own environment – by gaining a deeper understanding of learners. To gain this perspective, librarians increasingly must adopt the role of researcher.
Speech activity detection fusing acoustic phonetic and energy features

With the wider deployment of automatic speech recognition (ASR) systems, the importance of robust speech activity detection has been elevated both as a means of reducing band-width in client/server ASR and for overall system stability from barge-in through the recognition process. In this paper we investigate a novel technique for speech activity detection, that we have found to be effective in handling non-stationary noise events without negatively impacting the recognition process. This technique is based on combining acoustic phonetic likelihood based features with energy features extracted from the signal waveform. Reported results on two speech activity detection tasks demonstrate that the proposed method outperforms techniques which rely solely on acoustic or energy features.
Design a Virtual Object Representing Human-Machine Interaction for Music Playback Control in Smart Home

Intelligent home appliances and friendly human-machine interface design can provide a more comfortable living space for residents in smart home. However, excessive home automation sometimes may lose the sense of reality on operating home appliances. Virtual objects in smart home are not real home furnishings, and equipments, but it has the ability of actual home appliances or equipment to provide relevant home services through ubiquitous computing, virtual human-machine interface. That is, virtual objects retain the original features of appliance operation without losing high-technical and home-automation utilization on it. In this paper, we will design a virtual object being friendly human-machine interaction to control the music playback service in smart home. The virtual object is called virtual compact disc (CD) album, Virtual-CD. The residents can take a Virtual-CD, and do some operating gestures and entity movements on it, and then the music will be played, paused, and stopped according to various operations of virtual object in smart home. By the way, the resident can easily enjoy high-technical home service without losing traditional appliance operation in smart home.
Regularized kriging as a generalization of simple and universal kriging

We explore the possible advantages of relaxing the universal kriging non-bias condition using the methodology of Support Vectors. This leads to a regularized problem with restrictions, in which the objective function is the traditional variance term plus a term that penalises the bias, and whose resolution gives rise to a continuum of solutions for different values of the regularizer, including as specific cases, simple kriging and universal kriging. The analysis also permits the identification of prediction points that will admit slack in the non-bias condition without adversely affecting the prediction. The simulations conducted demonstrate that when the process mean function is poorly specified and in situations with a significant percentage of outliers, regularized kriging tends to improve the results of ordinary kriging and may be an alternative. Due to the relationship between kriging, regularization networks and Gaussian processes, the previous considerations also apply to both the latter techniques.
CREDIBLE COMMITMENTS IN INTERFIRM RELATIONSHIPS : THE “ SHADOW OF THE FUTURE ” AND THE “ SHADOW OF THE PAST ”

Credible commitments in the form of specific investments are recognized as important building blocks of interfirm relationships. In the past, such commitments have frequently been analyzed from the perspective of how they “bond” the investor to particular courses of action. Our focus is on the effects of credible commitments on the receiver. First, we consider the possibility that the receiver can opportunistically expropriate credible commitments, due to their idiosyncratic nature. Then, drawing on emerging economic and sociological theory, we consider whether conditions exist under which the investor’s credible commitment may actually bond the receiver and reduce opportunism. Specifically, we propose that when relationships possess a future (“shadow of the future”) or a past (“shadow of the past”), the effect of credible commitments changes from that of expropriation to bonding. We test our hypotheses empirically by parallel analyses on each side of 198 matched buyer-supplier dyads. These parallel tests permit us to ascertain whether trading partners differ in their reports on the effects of credible commitments. The results show strong support for our hypotheses, but also reveal differences between buyers and suppliers. The implications for theory and practice are discussed.
Ecotype Evolution in Glossina palpalis Subspecies, Major Vectors of Sleeping Sickness

Background The role of environmental factors in driving adaptive trajectories of living organisms is still being debated. This is even more important to understand when dealing with important neglected diseases and their vectors. Methodology/Principal Findings In this paper, we analysed genetic divergence, computed from seven microsatellite loci, of 614 tsetse flies (Glossina palpalis gambiensis and Glossina palpalis palpalis, major vectors of animal and human trypanosomes) from 28 sites of West and Central Africa. We found that the two subspecies are so divergent that they deserve the species status. Controlling for geographic and time distances that separate these samples, which have a significant effect, we found that G. p. gambiensis from different landscapes (Niayes of Senegal, savannah and coastal environments) were significantly genetically different and thus represent different ecotypes or subspecies. We also confirm that G. p. palpalis from Ivory Coast, Cameroon and DRC are strongly divergent. Conclusions/Significance These results provide an opportunity to examine whether new tsetse fly ecotypes might display different behaviour, dispersal patterns, host preferences and vectorial capacities. This work also urges a revision of taxonomic status of Glossina palpalis subspecies and highlights again how fast ecological divergence can be, especially in host-parasite-vector systems.
Environmental damages in court: the "American trader" case

DEPARTMENT OF AGRICGI,TURAL RESOURCE AND ECONOMICS POLICY AND AYD RESOGRCES DIVISION OF AGRICULTURE NATURAL UNIVERSITY OF CAI~IFORNIA BERKELEY AT EYVIROYMEITAI. DAM4GES IN COURT: THEAMERICAN TKADER CASE David J. Chapman W. Michael Hanemann California . 4gricultural Experiment Station Giannini Foundation of Agricultural Economics October 2000