Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of fuzzy-AHP extent analysis to determine the relative importance of risk factors in operative mortality after Coronary Artery Bypass surgery

The goal of this study is to recognize most important risk factors associated with surgical mortality in patient who underwent CABG by the integration of fuzzy concept and analytical hierarchy process method to represent pairwise comparison of odd ratios and overcome ambiguities involved in the statistical data. A literature search from 1980 to January 2013 using the MEDLINE and Science Direct database is performed and data of the reported predictors were extracted. A fuzzy AHP model for comparing the relative importance of risk factors was developed. Moreover, fuzzy clustering method is applied to classify calculated weight of risk factors. The result indicated that advanced age (over 70 years), sever left ventricular ejection fraction (LVEF<30%), emergency state of patient, elevated creatinin (above 2 mg over dL) and reoperation have the highest calculated weight among other predictors. In addition to, other variables were identified to be contributing risk factors to operative mortality after CABG, although they have not reached the level of importance of core risk factors. This study also, showed that the importance of risk factors varied by geographic region. We conducted that fuzzy clustering and FAHP has successfully detected strongest risk factors to predict mortality rate after CABG and showed the power of the engineering tools in health area. Furthermore, developed model, as a decision support tool, can be helpful for surgeons to determine appropriate technique for better management of individual patient before surgery as well as to provide pertinent information to develop novel scoring model according to importance of risk factors in different regions.
Galois groups of Schubert problems via homotopy computation

Numerical homotopy continuation of solutions to polynomial equations is the foundation for numerical algebraic geometry, whose development has been driven by applications of mathematics. We use numerical homotopy continuation to investigate the problem in pure mathematics of determining Galois groups in the Schubert calculus. For example, we show by direct computation that the Galois group of the Schubert problem of 3-planes in ℂ 8 meeting 15 fixed 5-planes non-trivially is the full symmetric group S 6006 .
Bayesian Trigonometric Support Vector Classifier

This letter describes Bayesian techniques for support vector classification. In particular, we propose a novel differentiable loss function, called the trigonometric loss function, which has the desirable characteristic of natural normalization in the likelihood function, and then follow standard gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implement model adaptation, while keeping the merits of support vector classifier, such as sparseness and convex programming. This differs from standard gaussian processes for classification. Moreover, we put forward class probability in making predictions. Experimental results on benchmark data sets indicate the usefulness of this approach.
Mining time-dependent patient outcomes from hospital patient records

We describe REMIND, a data mining framework that accurately infers missing clinical information by reasoning over the entire patient record. Hospitals collect computerized patient records (CPR's) in structured (database tables) and unstructured (free text) formats. Structured clinical data in the CPR's is often poorly recorded, and information may be missing about key outcomes and processes. For instance, for a population of 344 colon cancer patients, important clinical outcomes, such as disease state and its evolution, are stored only as unstructured data (doctors' dictations) in the CPR. Raw evidence (extracted directly from the CPR) is not a good predictor of disease state. Yet by combining this evidence in a principled fashion (using methods from uncertain and temporal reasoning), REMIND accurately infers disease state sequences for recurrence, a complex time-varying outcome, for these patients. These outcomes can now be added back into the CPR in structured form.
MADIT‐I and MADIT‐II

The MADIT‐I study was a proof‐of‐principle study, and this randomized trial showed that the implantable cardioverter defibrillator (ICD) saves lives in high‐risk patients with coronary heart disease. The MADIT‐II study showed that prophylactic ICD therapy was associated with significantly improved survival in patients with ischemic cardiomyopathy, as defined by documented coronary heart disease and advanced left ventricular dysfunction, without requiring screening for ventricular arrhythmias or inducibility by electrophysiologic testing. Taken together, these two trials, as well as the results from several other randomized ICD trials, indicate that ICD therapy is indicated in coronary patients who meet MADIT‐I or MADIT‐II eligibility criteria and are not excluded by major noncardiac comorbidity. (J Cardiovasc Electrophysiol, Vol. 14, pp. S96‐S98, September 2003, Suppl.)
Creativity and Conformity in Science: Titles, Keywords and Co-word Analysis

In this paper the adequacy of the co-word method for mapping the structure of scientific inquiry is explored. Co-word analysis of both the keywords and the titles of a set of papers in `acidification research' is undertaken and the results are found to be comparable, though the keyword-derived results provide greater detail. This strongly suggests that keyword indexing doest not, as has sometimes been claimed, distort coword findings. It also points to differences between titles (which often emphasize the supposed originality of an article) and keywords (which tend to show the relationship between the paper and other publications). The paper also explores important differences between the methodological assumptions that underlie the Paris/Keele co-word clustering algorithms and the factor analysis method for creating clusters.
Ellen Berscheid, Elaine Hatfield, and the Emergence of Relationship Science

In the past 25 years, relationship science has grown from a nascent research area to a thriving subdiscipline of psychological science. In no small measure, this development reflects the pioneering contributions of Ellen Berscheid and Elaine Hatfield. Beginning at a time when relationships did not appear on the map of psychological science, these two scholars identified relationships as a crucial subject for scientific psychology and began to chart its theoretical and empirical territory. In this article, we review several of their most influential contributions, describing the innovative foundation they built as well as the manner in which this foundation helped set the stage for contemporary advances in knowledge about relationships. We conclude by discussing the broader relevance of this work for psychological science.
An Intersectional Analysis of International Relations: Recasting the Discipline

In this essay we use a basic feminist analytical tool, intersectionality, to understand why we do not see more women across the spectrum and at all levels in the international relations field in the United States. Our intersectional analysis reveals that to understand why women are underrepresented in IR, we should not look harder at women in IR but rather at IR as a discipline.
Are patches cutting it?: structuring distribution within a JVM using aspects

Distribution is hard to modularize. Consequently, its addition to a software system can jeopardize fundamental software engineering principles such as maintainability, understandability and evolvability. The distributed Java Virtual Machine (dJVM) is a cluster aware implementation of a JVM, designed specifically for evaluating distributed runtime support algorithms [1]. A prototype implementation of the dJVM relies on a patch file applied to IBM's Jikes Research Virtual Machine (RVM) [6], introducing distribution code into roughly 55% of the original 1500 files.An initial experiment using AspectJ [7] to introduce this same distribution code as aspects demonstrates the benefits of a modularized approach versus the original patched approach. Preliminary results show that aspects can improve the overall quality of the implementation from a software engineering perspective. Specifically, the aspects improved the internal structure of distribution code and made its external interaction explicit. Additionally, consolidating and structuring previously scattered code reduced its size by a factor of three.
POST-FORDIST CAPITALISM IN THE CZECH REPUBLIC:THE INVESTMENT OF FLEXTRONICS IN BRNO

Drawing on the author's case study of one 'unsuccessful' attempt of the Czech Republic and Brno to lure foreign capital to the city of Brno, this arti- cle attempts to provide incentives for rethinking the theories of post-Fordism in order to theorise about the particular, local articulation of capitalism(s) in East Central Europe. The author presents selected findings from the case study with the intention of introducing and demonstrating the utility of the form-analytic, strategic-relational approach and some of the post-Fordist theories in analysing capitalism and socio-economic governance in the Czech Republic. He draws on the case of the subsidised investment in the Brno region by the transnational cor- poration Flextronics in 2000-2003. The company left the locality after approxi- mately two years, an unprecedented occurrence in the Czech Republic, which re- sulted in some political upheaval. The author focuses on the interaction between the investor and the state and other bodies of socio-economic governance and ar- gues that the story of Flextronics is indicative of some of the post-Fordist trans- formations in capitalism and capitalist governance. Further, the author also draws some conclusions with regard to the relations between company, state, and municipality in the post-Fordist environment in the Czech Republic. Sociologický casopis/Czech Sociological Review, 2004, Vol. 40, No. 3: 343-362
Essential Role of Neural Wiskott-Aldrich Syndrome Protein in Neurite Extension in PC12 Cells and Rat Hippocampal Primary Culture Cells*

Neural Wiskott-Aldrich syndrome protein (N-WASP) is an actin-regulating protein that induces filopodium formation downstream of Cdc42. It has been shown that filopodia actively extend from the growth cone, a guidance apparatus located at the tip of neurites, suggesting their role in neurite extension. Here we examined the possible involvement of N-WASP in the neurite extension process. Since verprolin, cofilin homology andacidic region (VCA) of N-WASP is known to be required for the activation of Arp2/3 complex that induces actin polymerization, we prepared a mutant (Δcof) lacking four amino acid residues in the cofilin homology region. The corresponding residues in WASP had been reported to be mutated in some Wiskott-Aldrich syndrome patients. Expression of Δcof N-WASP suppressed neurite extension of PC12 cells. In support of this, the VCA region of Δcof cannot activate Arp2/3 complex enough compared with wild-type VCA. Furthermore, H208D mutant, which has been shown unable to bind to Cdc42, also works as a dominant negative mutant in neurite extension assay. Interestingly, the expression of H208D-Δcof double mutant has no significant dominant negative effect. Finally, the expression of the Δcof mutant also severely inhibited the neurite extension of primary neurons from rat hippocampus. Thus, N-WASP is thought to be a general regulator of the actin cytoskeleton indispensable for neurite extension, which is probably caused through Cdc42 signaling and Arp2/3 complex-induced actin polymerization.
High Technology Entrepreneurs versus Small Business Owners in Israel

In recent years there has been growing interest in the psychology of managers and entrepreneurs. Yet, few studies have compared the two, and the literature often treats them interchangeably. This exploratory study focused on two types of entrepreneurs: high technology entrepreneurs (HTEs) and small business owners (SBOs) who were assumed to be more similar to managers. An attempt to identify psychological similarities and differences between them was made. Twenty-five HTEs and fifty SBOs were interviewed with respect to their family and professional backgrounds, self-perception and work attitudes. Results revealed a number of similarities (high involvement, optimism, energy, independence and initiative) as well as differences (HTEs' higher level of education, higher military rank and greater leadership experience as compared to the SBOs' greater realism, greater love of management and greater likelihood of having a father who was also an entrepreneur). These differences were interpreted within a psychoanalytic framework that focuses attention on SBOs' positive identification with both parents as compared to the HTEs' negative identification with father and greater identification with work. Practical implications of these differences for the management of HTEs' and SBOs' organisations or businesses and for public policy were drawn.
Significance Testing Against the Random Model for Scoring Models on Top K Predictions

Performance at top k predictions, where instances are ranked by a (learned) scoring model, has been used as an evaluation metric in machine learning for various reasons such as where the entire corpus is unknown (e.g., the web) or where the results are to be used by a person with limited time or resources (e.g., ranking financial news stories where the investor only has ti me to look at relatively few stories per day). This evaluation metric is primarily us ed to report whether the performance of a given method is significantly better than other (baselin e) methods. It has not, however, been used to show whether the result is significant when compared to the simplest of baselines — the random model. If no models outperform the random model at a given confidence interval, then the results may not be worth reporting. This paper introduces a technique to perform an analysis of the expected performance of the top k predictions from the random model given k and a p-value on an evaluation dataset D. The technique is based on the realization that the distribu tion of the number of positives seen in the top k predictions follows a hypergeometric distribution, which has welldefined statistical density functions. As this distributio n is discrete, we show that using parametric estimations based on a binomial distribution are almost always in complete agreement with the discrete distribution and that, if they differ, an interpol ation of the discrete bounds gets very close to the parametric estimations. The technique is demonstrated on results from three prior published works, in which it clearly shows that even though performance is greatly increased (sometimes over 100%) with respect to the expected performance of the random model (at p = 0.5), these results, although qualitatively impressive, are not always as signi ficant ( p = 0.1) as might be suggested by the impressive qualitative improvements. The technique is used to show, given k, both how many positive instances are needed to achieve a specific sign ificance threshold is as well as how significant a given top k performance is. The technique when used in a more global setting is able to identify the crossover points, with respect to k, when a method becomes significant for a given p. Lastly, the technique is used to generate a complete confide nce curve, which shows a general trend over all k and visually shows where a method is significantly better tha n the random model over all values of k.
Minimal important difference of the transition dyspnoea index in a multinational clinical trial

Dyspnoea is a primary symptom of chronic obstructive pulmonary disease (COPD). The baseline (BDI) and transition (TDI) dyspnoea indices are commonly used instruments to assess breathlessness and the impact of intervention. Its validity and pattern of response in multinational clinical trials has not been established. In a retrospective analysis of a cohort of 997 COPD patients who received tiotropium, salmeterol or placebo, in addition to usual care, the validity and pattern of response of the BDI and TDI were examined. The BDI was significantly correlated with the dyspnoea diary (DD) score and the symptom and activity components of the St. George's respiratory questionnaire (SGRQ), establishing concurrent validity. Furthermore, the TDI was also correlated with the changes in DD, SGRQ symptom and activity scores. Construct validity was established by the association between baseline forced expiratory volume in one second (FEV1) and BDI and ΔFEV1 with TDI. Physician's global evaluation (PGE) was significantly associated with BDI as well as ΔPGE with TDI. Significant correlations have also been observed when the cohorts were classified according to native English and native nonEnglish speaking countries. A change in PGE of 1 category (i.e. 2 units on an 8‐point scale) was associated with a mean TDI of ∼1 unit (0.9–1.3 mean focal score), lending further support to the clinical significance of this change inherent in the instrument's descriptors. TDI responders (i.e. focal score ≥1 unit) used less supplemental salbutamol, had fewer exacerbations and had significantly improved health status as measured by impacts and total SGRQ scores compared with nonresponders. In conclusion, the transition dyspnoea index is a valid instrument when used in a multinational clinical trial and the patterns of response confirm a 1‐unit change in the transition dyspnoea index focal score as being clinically important.
Identifying Metaphorical Word Use with Tree Kernels

A metaphor is a figure of speech that refers to one concept in terms of another, as in “He is such a sweet person”. Metaphors are ubiquitous and they present NLP with a range of challenges for WSD, IE, etc. Identifying metaphors is thus an important step in language understanding. However, since almost any word can serve as a metaphor, they are impossible to list. To identify metaphorical use, we assume that it results in unusual semantic patterns between the metaphor and its dependencies. To identify these cases, we use SVMs with tree-kernels on a balanced corpus of 3872 instances, created by bootstrapping from available metaphor lists. 1 We outperform two baselines, a sequential and a vectorbased approach, and achieve an F1-score of 0.75.
Analyzing the Impact of Storage Shortage on Data Availability in Decentralized Online Social Networks

Maintaining data availability is one of the biggest challenges in decentralized online social networks (DOSNs). The existing work often assumes that the friends of a user can always contribute to the sufficient storage capacity to store all data. However, this assumption is not always true in today's online social networks (OSNs) due to the fact that nowadays the users often use the smart mobile devices to access the OSNs. The limitation of the storage capacity in mobile devices may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSNs can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction.
Whither development studies? Reflections on its relationship with social policy

This paper contributes to an ongoing conversation between development studies (DS) and social policy (SP) as academic fields, particularly in the UK. Using Andrew Abbott's analysis of the social sciences as an evolving system of knowledge lineages (KLs), it reflects on the status of DS and its relationship with SP. Defining DS as a distinctive KL centred on critical analysis of ideas and projects for advancing human well-being, I suggest that it has lost coherence even as research into international development thrives. Indeed it is easy to envisage its gradual assimilation into other KLs, including SP. The two increasingly overlap in their analysis of the causes of relative poverty and injustice, and what can be done to address them, within countries and globally. Strengthening links between the two fields can be justified as a political project, even at the risk of some loss of plurality and plenitude across the social sciences.
The stellar content of the Hamburg/ESO survey , IV. Selection of candidate metal-poor stars

We present the quantitative methods used for selecting candidate metal-poor stars in the Hamburg/ESO objective-prism survey (HES). The selection is based on the strength of the Ca II K line, B - V colors (both measured directly from the digital HES spectra), as well as J - K colors from the 2 Micron All Sky Survey. The KP index for Ca II K can be measured from the HES spectra with an accuracy of 1.0 angstrom, and a calibration of the HES B - V colors, using CCD photometry, yields a 1-sigma uncertainty of 0.07 mag for stars in the color range 0.3 -2.0 without sacrificing completeness at the lowest metallicities. A test of the selection using 1121 stars of the HK survey of Beers, Preston, and Shectman present on HES plates suggests that the completeness at [Fe/H] -2.5 and 97% of all stars with [Fe/H] > -2.0 are rejected. The selection was applied to 379 HES fields, covering a nominal area of 8853 deg(2) of the southern high Galactic latitude sky. The candidate sample consists of 20 271 stars in the magnitude range 10 less than or similar to B less than or similar to 18. A comparison of the magnitude distribution with that of the HK survey shows that the magnitude limit of the HES sample is about 2mag fainter. Taking the overlap of the sky areas covered by both surveys into account, it follows that the survey volume for metal-poor stars has been increased by the HES by about a factor of 10 with respect to the HK survey. We have already identified several very rare objects with the HES, including, e. g. , the three most heavy-element deficient stars currently known.
The Impact of the Web Prefetching Architecture on the Limits of Reducing User's Perceived Latency

Web prefetching is a technique that has been researched for years to reduce the latency perceived by users. For this purpose, several Web prefetching architectures have been used, but no comparative study has been performed to identify the best architecture dealing with prefetching. This paper analyzes the impact of the Web prefetching architecture focusing on the limits of reducing the user's perceived latency. To this end, the factors that constrain the predictive power of each architecture are analyzed and these theoretical limits are quantified. Experimental results show that the best element of the Web architecture to locate a single prediction engine is the proxy, whose implementation could reduce the perceived latency up to 67%. Schemes for collaborative predictors located at diverse elements of the Web architecture are also analyzed. These predictors could dramatically reduce the perceived latency, reaching a potential limit of about 97% for a mixed proxy-server collaborative prediction engine