Miracle-dz/UniFAR-FTUs huggingface dataset:
Europeanization, EU Conditionality and Governance Quality: Empirical Evidence on Central and Eastern European Countries

A large body of work within the Europeanization and conditionality literature has examined EU’s effect on governance quality in Central and Eastern European countries (CEECs). This article aims to contribute to existing work by addressing two questions. First, was EU’s accession conditionality conducive to better governance in CEECs or was it designed to pick up already committed reformers? Secondly, during which period was the EU more influential on governance quality in CEECs: accession or membership? To address these questions, we propose a simple model of moral hazard and adverse selection; and draw on survey-based governance quality data to test its predictions. We report that the EU has offered candidate status to countries that already had better governance quality relative to their peers before 1997. Nonetheless, EU conditionality also had some positive effects on CEEC governance during the accession period, but this effect tended to disappear during the membership period. On the basis of these findings, we argue that EU’s impact on governance quality in CEECs has to be examined in conjunction with path dependence and therefore the perceived risk of ‘backsliding’ requires more detailed analysis.
Comparison of ropivacaine and fentanyl toxicity in human fibroblasts

Introduction Although ropivacaine and fentanyl are commonly administered intra-articularly after knee or shoulder arthroscopy for postoperative pain control, there are no studies investigating the toxicity of ropivacaine and fentanyl on human fibroblasts (hF). Material and methods Human fibroblasts were seeded in monolayer triple flasks at a density of 104 cells/cm2 and plated into 96 plates at a density of 5000 cells per well. After fully aspirating the culture medium 200 µl of ropivacaine or fentanyl in its corresponding concentration or culture medium only was added to each well. After 30 min ropivacaine or fentanyl was removed and fresh culture medium was added. Fibroblast mitochondrial activity and apoptosis marker level were evaluated after 1 h, 24 h and 7 days. Results We found a significant decrease in mitochondrial activity after 7 days when exposed to 0.5% ropivacaine. Mitochondrial activity after 1 h, 24 h and 7 days was significantly decreased when fibroblasts were exposed to 0.05% fentanyl. Also, a significant decrease in mitochondrial activity was observed 1 h after exposure to 0.025% fentanyl. Cell viability remained unchanged at any other point in time. A significant increase of caspase-3, as a marker of apoptosis, was only present after exposure to 0.5% ropivacaine after 7 days. Conclusions These data suggest that both drugs have a concentration-dependent effect on mitochondrial activity in hF in vitro. This effect is more pronounced with fentanyl. Because the cytotoxicity of fentanyl, without the anticipated increase of caspase-3 as an apoptosis marker, remains unclear, we cannot support fentanyl as an alternative to ropivacaine.
The degree of difficulty of embryo transfer is an independent factor for predicting pregnancy.

BACKGROUND The role of embryo transfer as regards the success of IVF/ICSI treatments is recognized but has not been comprehensively evaluated. In order to determine its importance, the degree of difficulty of 4807 embryo transfers after IVF/ICSI was analysed retrospectively. METHODS Logistic regression analysis identified the age of the subject, type of treatment (IVF versus ICSI), number of embryos transferred and degree of difficulty of embryo transfer as independent factors predicting pregnancy. The main focus of the study was to evaluate the importance of the difficulty of embryo transfer after taking into account the other confounding variables. RESULTS Embryo transfer was classified as easy (2821), intermediate (1644) or difficult (342). The transfer was considered difficult if it was time consuming, the catheter met great resistance, there was a need to change the catheter, if sounding or cervical dilatation was needed or if blood was found in any part of the catheter. Easy or intermediate transfers resulted in a 1.7-fold higher pregnancy rate than difficult transfers (P < 0.0001; 95% confidence interval: 1.3-2.2). CONCLUSIONS This study demonstrates that the degree of difficulty of embryo transfer is an independent factor as regards achieving pregnancy after IVF/ICSI. All efforts should be made to avoid difficult embryo transfers. Physicians should be alert to the factors associated with embryo transfer and should be instructed to use a stepwise approach in difficult transfers.
The Evolution of Landsat Data Analysis 1

Preface. In this paper a description is presented of how the multispectral data analysis technology, which has come to be synonymous with Landsat, was begun and how it developed and spread among the broader research and user community. The paper is concluded with brief remarks about key factors which moderated the development and what the future may hold for its further development. To describe a 25 year long activity as varied and complex as the evolution of the Landsat related data analysis is a daunting task. Many significant events must be omitted and others only briefly mentioned. Example activities have been chosen which occurred early and led to perhaps the largest impact in the development of the technology. Background: Setting the Stage. The people of the U. S. and much of the world were surprised, one might even say shocked, when the Soviet Union successfully launched the first Earth orbiting artificial satellite in October 1957. The impact on the U. S. was immediate and substantial, affecting tangibles such as the U. S. education system, but also intangibles such as how this society thought of itself. One of the significant responses to this event was the National Aeronautics and Space Act in 1958 which created NASA from the National Advisory Committee on Aeronautics. These events resulted in people in various organizations beginning to consider how an ability to operate in space could be used.
Dexterous robotic hand grasp modeling using piecewise linear dynamic model

Learning from sensor data is important in many robotic research areas, such as dexterous robotic hand grasping. In this paper, a piecewise linear dynamic model is proposed for analyzing robotic hand grasp. The combination of linear dynamic model and the switched systems can achieve better results in grasp learning due to its advantage of modeling multi-phase grasping process. To the best knowledge of the authors, this is the first time for piecewise linear dynamic model to be incorporated into the framework of modeling robotic hand grasp process. The performance of the proposed model is evaluated on our experimental system and shows promising results.
Inventories, Sticky Prices and the Propogation of Nominal Shocks

Post-war business cycle fluctuations of output and inflation are remarkably persistent. Many recent sticky-price monetary business cycle models, however, grossly underpredict this persistence. We assess whether adding inventories to a standard sticky-price model raises the persistence of output and inflation. For this addition, we consider three different frameworks: a linear-quadratic inventory model, a factor of production model, and a shopping-cost model. We find that adding inventories increases the persistence of output and inflation, but that the increase is smaller for inflation. Overall, the shopping-cost model best explains the persistence of output and inflation.
Combining analytic phase and Fourier phase for face recognition

This paper proposes a Borda count based approach to combine the complimentary edge information present in spatial domain and Fourier domain representation of image for face recognition. The edges in spatial domain are captured using analytic phase based representation. The phase of Fourier transform is used to represent the edge information of image in Fourier domain. The experimental results show that performance of the proposed approach is better then the several existing approaches, as well as from the performances obtained using either analytic phase image or phase of the Fourier transform.
Robust Support Vector Machine

Support Vector Machine (SVM) is a well-known kernel-based method for binary classification problem. SVM aims at constructing the optimal middle hyperplane which induces the largest margin. It is proven that in a linearly separable case, this middle hyperplane offers the high accuracy on universal datasets. However, real world datasets often contain overlapping regions and therefore, the decision hyperplane should be adjusted according to the profiles of the datasets. In this paper, we propose Robust Support Vector Machine (RSVM), where the hyperplanes can be properly adjusted to accommodate the real world datasets. By setting the value of the adjustment factor properly, RSVM can handle well the datasets with any possible profiles. Our experiments on the benchmark datasets demonstrate the superiority of the RSVM for both binary and one-class classification problems.
Return Value Prediction in a Java Virtual Machine

We present the design and implementation of return value prediction in SableVM, a Java Virtual Machine. We give detailed results for the full SPEC JVM98 benchmark suite, and compare our results with previous, more limited data. At the performance limit of existing last value, stride, 2-delta stride, parameter stride, and context (FCM) sub-predictors in a hybrid, we achieve an average accuracy of 72%. We describe and characterize a new table-based memoization predictor that complements these predictors nicely, yielding an increased average hybrid accuracy of 81%. VM level information about data widths provides a 35% reduction in space, and dynamic allocation and expansion of per-callsite hashtables allows for highly accurate prediction with an average per-benchmark requirement of 119 MB for the context predictor and 43 MB for the memoization predictor. As far as we know, the is the first implementation of non-trace-based return value prediction
Unsupervised discovery of basic human actions from activity recording datasets

Human Behavior Understanding (HBU) is a major challenge facing intelligent agents. Most approaches to solve this problem assume a recognition/detection context in which the agent/robot tries to match the perceived behavior to one or more predefined motion patterns (e.g. walking, running etc). A more challenging problem is discovering these motion patterns without apriori assumption about the motions in the data, their duration or their numbers. This paper proposes the utilization of a novel motif discovery algorithm based on the exact MK algorithm to discover basic actions in activity records. The proposed system was evaluated on real records of full body motions and is shown in this paper to achieve high accuracy compared with a recently proposed motif discovery algorithm applied to the same dataset.
Query Processing in Paraconsistent Databases in the Presence of Integrity Constraints

In this paper, we present an approach to query processing for paraconsistent databases in the presence of integrity constraints. Paraconsistent databases are capable of representing positive as well as negative facts and typically operate under the open world assumption. It is easily observed that integrity constraints are usually statements about negative facts and as a result paraconsistent databases are suitable as a representation mechanism for such information. We use set-valued attributes to code large number of regular tuples into one extended tuple (with set-valued components). We define an extended relational model and algebra capable of representing and querying paraconsistent databases in the presence of integrity constraints. The extended algebra is used as the basis for query processing in such databases.
Prosodic features based on wavelet analysis for speaker verification

Most conventional speaker recognition systems rely on short-term spectral information. But they ignore the long-term information such as prosody which also conveys speaker information. In this paper, we propose an approach that extracts prosodic features based on long-term information. First, by making wavelet analysis, we can reveal the trends of the f0 and energy contour. Subsequently, the prosodic features are extracted only from approximation coefficients. We use these features in a GMM-UBM based text-independent speaker verification system. The proposed method achieves an EER of 23.3% on the NIST2004 8sides-1side task scheme. This result is promising while the baseline system, which uses short-term f0 feature, only results in an EER of 33.49% in this task.
Learning a Replacement Model for Query Segmentation with Consistency in Search Logs

Query segmentation is to split a query into a sequence of non-overlapping segments that completely cover all tokens in the query. The majority of methods are unsupervised, however, they are usually not as accurate as supervised methods due to the lack of guidance from labeled data. In this paper, we propose a new paradigm of learning a replacement model with consistency(LRMC), to enable unsupervised training with guidance from search log data. In LRMC, we first assume the existence of a base segmenter (an implementation of any existing approach). Then, we utilize a key observation that queries with a similar intent tend to have consistent segmentations, to automatically collect a set of labeled data from the outputs of the base segmenter by leveraging search log data. Finally, we employ the auto-collected data to train a replacement model for selecting the correct segmentation of a new query from the outputs of the base segmenter. The results show LRMC can improve state-of-the-art methods by an F-Score of around 7%.
Feature extraction of protein secondary structure using 2D convolutional neural network

In this paper we propose a approach using 2D convolutional neural network(CNN) for the prediction of protein secondary structure. A representation of two dimensional PSSM is directly used to convolve with filters to extract the features. This new representation reflects not only the evolutionary information but also the sequence interaction of residue. The architecture of convolutional neural network has two convolutional layers and one max-pooling layer. The feature maps extracted from second convolutional layer are used to feed to Bayes classifier, in order to build prediction model. The Q3 accuracy 77.7% of 25PDB dataset is achieved based on 3 fold cross validation experiments using CNN features. The performance based on CNN features is better than 73.8% of 25PDB dataset using the original features. Experimental results illustrate that CNN features improve the prediction performance and have the potential ability of the invariance against mutation, insertion, deletion of residue.
Time- frequency based renyi entropy of heart rate variability for newborn seizure detection

The time-frequency (TF) version of Renyi entropy, which measures the information content and complexity of a signal, is used here as a feature in the classification of the newborn heart rate variability (HRV) as either corresponding to seizure or non-seizure. The newborn HRV is initially mapped to the TF domain using the modified B distribution (MBD). The time-frequency distribution (TFD) of HRV is post-processed before the Renyi entropy is computed. This post-processing method uses an image processing technique called component linking to identify the true HRV components and localize them in the TF plane. The results obtained so far show that the HRV corresponding to non-seizure can be discriminated from those corresponding to seizure using TF-based Renyi entropy with 78.57% sensitivity and 83.33 % specificity.
Computable Features Required to Evaluate the Efficacy of Drugs and a Universal Algorithm to Find Optimally Effective Drug in a Drug Complex

Background The H1N1 pandemic in 2009 and the H5N1 pandemic in 2005 demonstrated that the drugs approved to treat influenza A viruses have low efficacy. This provided a stimulus for new studies of influenza A viruses in the context of the methods used in drug design developed over the past 100 years. Finding new universal drugs is the ultimate goal but its long time horizon is incompatible with emergency situations created by reoccurring influenza outbreaks. Therefore, we propose a computer-aided method for finding efficacious drugs and drug complexes based on the use of the DrugBank database. Methods (1) We start by assembling a panel of target proteins. (2) We then assemble a panel of drugs. (3) This is followed by a selection of benchmark binding pockets based on the panel of target proteins and the panel of drugs. (4) We generate a set of computational features, which measure the efficacy of a drug. (5) We propose a universal program to search for drugs and drug complexes. (6) A case study we report here illustrates how to use this universal program for finding an optimal drug and a drug complex for a given target. (7) Validation of the Azirchromycin and Aspirin complex is provided mathematically. (8) Finally, we propose a simple strategy to validate our computational prediction that the Azirchromycin and Aspirin complex should prove clinically effective. Result A set of computable features are mined and then based on these features, a universal program for finding the potential drug &drug complexes is proposed. Using this universal program, the Azirchromycin and Aspirin complex is selected and its efficacy is predicted mathematically. For clinical validation of this finding, future work is still required.
New Therapeutics in Promoting and Modulating Mandibular Growth in Cases with Mandibular Hypoplasia

Children with mandibular growth deficiency may develop airway obstruction. The standard treatment of severe airway obstruction involves invasive procedures such as tracheostomy. Mandibular distraction osteogenesis has been proposed in neonates with mandibular deficiency as a treatment option to avoid tracheostomy procedure later in life. Both tracheostomy and distraction osteogenesis procedures suffer from substantial shortcomings including scarring, unpredictability, and surgical complications. Forward jaw positioning appliances have been also used to enhance mandible growth. However, the effectiveness of these appliances is limited and lacks predictability. Current and future approaches to enhance mandibular growth, both experimental and clinical trials, and their effectiveness are presented and discussed.
Co-evolving code-related and database-related changes in a data-intensive software system

Current empirical studies on the evolution of software systems are primarily analysing source code. Sometimes, social aspects such as the activity of contributors are considered as well. Very few studies, however, focus on data-intensive software systems (DISS), in which a significant part of the total development effort is devoted to maintaining and evolving the database schema. We report on early results obtained in the empirical analysis of the co-evolution between code-related and database-related activities in a large open source DISS. As a case study, we have analysed OSCAR, for which the historical information spanning many years is available in a Git repository.
Kernel Neighborhood Preserving Embedding and its Essence Analysis

A new dimensionality reduction method, neighborhood preserving embedding (NPE) is recently proposed which offers a linear yet powerful method to preserve the local neighborhood structure on the data manifold. However, it is confined to linear transforms in the data space. For this, kernel NPE (KNPE) is presented, which preserves the local neighborhood structure in the higher-dimension feature space. To avoid computing the inverse matrix of the positive semi-definite kernel matrix, a transformed optimization problem and QR decomposition are used. Then the analysis on KNPE reveals that KNPE is equivalent to kernel principal component analysis (KPCA) plus NPE. The experimental results on the real-world data sets illustrate the effectiveness of the new algorithm.
Why Does the Randomized Clinical Trial Methodology So Often Mislead Clinical Decision Making?Focus on Moderators and Mediators of Treatment

FULL DISCLOSURE POLICY In accordance with the Accreditation Council for Continuing Medical Education’s Standards for Commercial Support, all CME providers are required to disclose to the activity audience the relevant financial relationships of the planners, teachers, and authors involved in the development of CME content. An individual has a relevant financial relationship if he or she has a financial relationship in any amount occurring in the last 12 months with a commercial interest whose products or services are discussed in the CME activity content over which the individual has control. Relationship information appears at the beginning of each CME-accredited article in this issue.
Earning-to-Price, Dividend-to-Price, Firm Growth and Stock Returns

Cooper, Gulen and Schill (2005) examine the cross-sectional relation between firm asset growth and subsequent stock returns over the period 1963-2003. They conclude that sorting US stocks on previous year asset growth rates is superior over other determinants of cross-sectional stock returns, such as: book-to-market, firm capitalization, momentum, amongst others. In this paper, I use the one-factor and the multi-factor models to investigate the ability of earnings-to-price, dividend-to-price, previous year asset growth rates, and retained-earnings-to-total-asset to explain the cross-sectional stock returns over the period 1980-2000. Inconsistent with Cooper et al. (2005), I conclude that sorting Jordanian firms on previous year asset growth rates is not superior over earnings-to-price and dividend-to-price. Overall, the dividend-to-price strategy appears to be the best strategy that helps in predicting the cross-section of Jordanian stock returns. JEL classification: G15
Bradykinin-Induced Microglial Migration Mediated by B1-Bradykinin Receptors Depends on Ca2+ Influx via Reverse-Mode Activity of the Na+/Ca2+ Exchanger

Bradykinin (BK) is produced and acts at the site of injury and inflammation. In the CNS, migration of microglia toward the lesion site plays an important role pathologically. In the present study, we investigated the effect of BK on microglial migration. Increased motility of cultured microglia was mimicked by B1 receptor agonists and markedly inhibited by a B1 antagonist, but not by a B2 receptor antagonist. BK induced chemotaxis in microglia isolated from wild-type and B2-knock-out mice but not from B1-knock-out mice. BK-induced motility was not blocked by pertussis toxin but was blocked by chelating intracellular Ca2+ or by low extracellular Ca2+, implying that Ca2+ influx is prerequisite. Blocking the reverse mode of Na+/Ca2+ exchanger (NCX) completely inhibited BK-induced migration. The involvement of NCX was further confirmed by using NCX+/− mice; B1-agonist-induced motility and chemotaxis was decreased compared with that in NCX+/+ mice. Activation of NCX seemed to be dependent on protein kinase C and phosphoinositide 3-kinase, and resultant activation of intermediate-conductance (IK-type) Ca2+-dependent K+ currents (IK(Ca)) was activated. Despite these effects, BK did not activate microglia, as judged from OX6 staining. Using in vivo lesion models and pharmacological injection to the brain, it was shown that microglial accumulation around the lesion was also dependent on B1 receptors and IK(Ca). These observations support the view that BK functions as a chemoattractant by using the distinct signal pathways in the brain and, thus, attracts microglia to the lesion site in vivo.