Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling with monads: extensible modeling semantics as syntactic sugar

We present an extensible implementation of Modelica-style modeling semantics. Modeling features are implemented using an intuitive encoding as an extensible state monad. Monadic computation naturally yields model composition and interpretation. This in turn allows for a clear separation between the modular aspects of a modeling language (e.g. classes and inheritance) and the symbolical and numerical treatment of models: While the former can be implemented by a compiler or interpreter, the latter can be moved into a core-library to be maintained by domain-experts. In a second step, we show how the elaboration aspects of the language can be removed by a correct and complete desugaring procedure. The residual language is a simple extension of a call-by-value λ-calculus. Thus it becomes possible to implement a compiler for a modeling language using existing standard techniques.
Development and validation of visions about future in early adolescence

ABSTRACT This article reports the development and psychometric requisites of Visions About Future (VAF), a measure to assess optimism, pessimism, and hope in early adolescence. Two different studies involving Italian middle school students were conducted. With the first study, the items were developed and the factor structure verified by using item analysis, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA); further, convergent and concurrent validity by testing hypothesised relationships with existing measures, were assessed. In the second study, we provided measurement invariance across gender among middle school students. Results provide strong psychometric support for VAF as a valid measure to explore propensity to optimism, pessimism and hope in early adolescence and for career education and career counselling activities.
Biometrics Evaluation Under Spoofing Attacks

While more accurate and reliable than ever, the trustworthiness of biometric verification systems is compromised by the emergence of spoofing attacks. Responding to this threat, numerous research publications address isolated spoofing detection, resulting in efficient counter-measures for many biometric modes. However, an important, but often overlooked issue regards their engagement into a verification task and how to measure their impact on the verification systems themselves. A novel evaluation framework for verification systems under spoofing attacks, called expected performance and spoofability framework, is the major contribution of this paper. Its purpose is to serve for an objective comparison of different verification systems with regards to their verification performance and vulnerability to spoofing, taking into account the system's application-dependent susceptibility to spoofing attacks and cost of the errors. The convenience of the proposed open-source framework is demonstrated for the face mode, by comparing the security guarantee of four baseline face verification systems before and after they are secured with antispoofing algorithms.
Pragmatic postmodernism and engagement through the culture of continuous creativity

ABSTRACT In order to engage students who have difficulty with the dominant world-view presented by accounting, or who have negative views of accounting, a different approach to teaching is required. We developed the concept of pragmatic postmodernism in the teaching of accounting to merge a postmodern teaching philosophy with the constraints and realities of teaching accounting. It represents an attempt to synthesise the individual learning needs of contemporary students who have difficulty accepting the dominant world-view of accounting with the constraints inherent in the resourcing, architecture and systems surrounding the teaching of accounting. The purpose of this research is to describe the thinking behind a pragmatic postmodern approach to the teaching of accounting. We provide examples of teaching approaches consistent with pragmatic postmodernism, that we have used successfully in our teaching to engage students who might otherwise be disengaged in their learning because of their rejection of the dominant world-view of accounting. In keeping with the philosophical underpinnings of postmodernism, we do not offer a magical panacea but an approach to thinking about teaching that seeks to prioritise the individual learning experience while remaining cognisant of the many constraints on the teaching of accounting.
An efficient algorithm for extraction of anatomical structures in retinal images

This paper presents efficient methods for automatic detection and extraction of blood vessels and optic disc (OD) both of which are two prominent anatomical structures in ocular fundus images. The blood vessel extraction algorithm is composed of four steps, i.e., matched filtering, local entropy-based thresholding, length filtering, and vascular intersection detection. The OD identification involves the efficient active contour (snake) method. Specifically, we use intensity variation and local contrast to initialize the snake model. Simulation results on a set of retinal images verify the effectiveness of the proposed methods.
Applying Pattern Theory in the Effective Management of Virtual Projects

The management of virtual projects is fundamentally different from that of traditional projects. Furthermore, the research in this area comes from different reference disciplines and perspectives, and a unified view or theory of best practices does not yet exist. We use the theoretical frame of patterns to propose a unified view. We focus on three concepts as the underlying theoretical elements for identifying patterns of effectiveness in virtual project management: (1) coordination, (2) communication, and (3) control. As a first step in the identification of specific patterns, we conducted a series of virtual focus groups, with participants from industry who had real experience with virtual projects. The brainstorming data from the focus groups was analyzed to develop an initial set of patterns. Based on this first step, we also present a structured process for the discovery and continuing validation of patterns of effectiveness in virtual projects, and discuss the issues involved in applying the process.
Trade induced technical change? The impact of Chinese imports on technology and employment

Despite popular belief among politicians and the public, the consensus amongst empirical economists is that trade has not been a major cause of increased wage inequality in advanced countries and the technological and institutional change are much more important. Recent theoretical work shows how trade can induce technical change, however. Furthermore, the consensus was reached using data predating the rise of China in the 1990s. In this paper, we examine the impact of the growth of Chinese imports on a panel of 30,000 establishments in 14 European countries. We find that Chinese import competition is associated with a significant increase in the adoption of new technology (measured by usage of information technology) and the generation of innovation (measured by patenting intensity). We also find that exposure to trade with China significantly increases the probability of establishment exit and reduces employment growth, but this effect is significant only for low-tech firms. The wave of Chinese trade has therefore caused a within and between plant increase in average IT intensity. Despite these effects on the intensive and extensive margins, we calculate that trade with China still only accounts for a relatively small proportion of the increase in IT intensity and patenting so does not appear to overturn the conventional wisdom that trade is less important than technical change.
Affirmative Consent Endorsement and Peer Norms Supporting Sexual Violence Among Vulnerable Students on College Campuses

Understanding how students endorse affirmative consent in their sexual relationships is essential to sexual violence prevention. Some research has indicated that LGBT students and students with disabilities may negotiate and endorse consent uniquely because of socially constructed traditional sexual scripts. Research indicates gender differences may exist as well. The proposed research examines differences based on gender, LGBT status, and disability in affirmative consent endorsement and peer norms around sexual violence. Results indicated that women, nonbinary students, LGBT students, and students with disabilities were significantly less likely than their privileged counterparts to indicate low endorsement of affirmative consent. Results also indicated that women and some LGBT students are significantly less likely than their privileged counterparts to indicate high peer norms supporting sexual violence. Limitations, implications, and future directions are discussed. AFFIRMATIVE CONSENT AMONG VULNERABLE STUDENTS ii
Analysis of T-wave morphology from the 12-lead electrocardiogram for prediction of long-term prognosis in patients initiating haemodialysis.

BACKGROUND Cardiovascular disease remains the most common cause of death in end-stage renal disease (ESRD). Recently, novel descriptors of T-wave morphology have been suggested as measures of repolarization heterogeneity and adverse prognosis in non-uraemic populations. However, whether these T-wave descriptors provide prognostic information in uraemic populations has not been examined. The present study aimed to determine the prognostic value of novel T-wave morphology variables in predicting total, cardiovascular and arrhythmia-related mortality in ESRD patients initiating haemodialysis. METHODS The study was a retrospective cohort of adult ESRD patients starting haemodialysis between 1998 and 2005; follow-up was until September 2006. A total of 325 patients were studied. Novel ECG variables characterizing repolarization and the T-wave loop were analysed. RESULTS Of 325 patients with technically analysable data, 154 (47.4%) died after a mean follow-up of 25.5+/-21.7 months. Direct comparison between cardiovascular death and non-cardiovascular death patients showed that the relative T-wave residuum (TWR) predicted cardiovascular mortality (0.20+/-0.21% vs 0.24+/-0.17%, P=0.005). In Cox modeling, relative TWR was an independent predictor of cardiovascular [relative risk (RR)=1.86; P=0.013] and arrhythmia-related mortality (RR=2.102; P=0.012). CONCLUSIONS The heterogeneity of myocardial repolarization, measured by the relative T-wave residuum in the ECG, appears to be an independent predictor of cardiovascular and arrhythmia-related mortality in patients initiating haemodialysis.
New planning benchmarks: vehicle routing problem variants

The Vehicle Routing Problem (VRP) is a classical problem in Operations Research. It is a generalisation of the Travelling Salesman Problem in which a set of locations must be visted by one of a number of vehicles. All vehicles start and end at a depot location. The aim of the problem is to minimise the total distance covered by the trucks. There are many different variants of the VRP. For example, Capacitated VRP, VRP with Time Windows, VRP with Multiple Depots and VRP with Pickup and Delivery. All of these variants are interesting planning problems, as they deal with resource management, time windows, routing, etc. In this work, we model some of the variants of VRP in PDDL. We provide this as an intersting new benchmark set for planning. Already, many planning benchmarks are based in some way on transportation. Modelling VRP directly allows access to a great quantity of existing benchmark instances. This has the benefit of posing difficult and realistic challenges to existing planners.
Cool Facts About Cooling Electric Motors: Improvements in Applications That Fall Outside the Normal Operating Conditions

The evolution of electric motor design as it pertains to cooling methods provides insights about better ways to cool machines in service. The array of methods engineers have devised to solve the same problems are fascinating yet reassuring because many things remain unchanged even after a century of progress. This article discusses how motors are cooled and how heat dissipation can be improved for applications that fall outside the normal operating conditions defined by the National Electrical Manufacturers Association (NEMA) Standard MG 1 [1].
A Spectral Clustering Approach Based on Modularity Maximization for Community Detection Problem

Modularity is widely-used objective function to detect communities and there are lots of algorithms based on modularity maximization. The leading eigenvector method is one of them where modularity is maximized by choosing the first eigenvector as partition result. To analyze in depth the information provided by other eigenvectors, modularity maximization could be transformed to vector partitioning problem. This paper proposes a method to find non-overlapping vertex vector sets so as to maximize the quadratic sum of norms of community vectors. We observe spatial distribution of the vertex vectors of networks and then discover two phenomenons. First, the vertex vectors belong to different communities are separated by an angle. Second, the node with a larger degree would correspond to a vertex vector with a larger norm. Based on these two phenomena, we design a heuristic community detection algorithm. When a network with weaker community structure, the over-partition problem is considered. The experiment results show that the proposed solution provides higher accuracy than other solutions.
Does antibacterial treatment for urinary tract infection contribute to the risk of breast cancer?

Low lignan status has been reported to be related to an elevated risk of breast cancer. Since lignan status is reduced by antibacterial medications, it is plausible to hypothesize that repeated use of antibiotics may also be a risk factor for breast cancer. History of treatment for urinary tract infection was studied for its prediction of breast cancer among 9461 Finnish women 19–89 years of age and initially cancer-free. During a follow-up in 1973–1991, a total of 157 breast cancer cases were diagnosed. Women reporting previous or present medication for urinary tract infection at baseline showed an elevated breast cancer risk in comparison with other women. The age-adjusted relative risk was 1.34 (95% confidence interval (CI) = 0.98–1.83). The association was concentrated to women under 50 years of age. The relative risk for these women was 1.74 (95% CI 1.13–2.68), whereas it was 0.97 (95% CI 0.59–1.58) for older women. The relative risk in the younger age-group was 1.47 (95% CI 0.73–2.97) during the first 10 years of follow-up, and 1.93 (95% CI 1.11–3.37) for follow-up times longer than 10 years. These data suggest that premenopausal women using long-term medication for urinary tract infections show a possible elevated risk of future breast cancer. The results are, however, still inconclusive and the hypothesis needs to be tested by other studies. © 2000 Cancer ResearchCampaign
The role of Ca 2 + influx in endocytic vacuole formation in pancreatic acinar cells

*Department of Cellular and Molecular Physiology, University of Liverpool, Crown Street, Liverpool L69 3BX, U.K. †Laboratory for Developmental Neurobiology, Riken Brain Science Institute, 2-1 Hirosawa, Wako City, Saitama 351-0198, Japan ‡Respiratory Therapy Area Unit, Medicines Research Centre, GlaxoSmithKline, Stevenage SG1 2NY, England, U.K. §NIHR Liverpool Pancreas Biomedical Research Unit, University of Liverpool, Crown Street, Liverpool L69 3BX, U.K.
Diffeomorphic matching of distributions: a new approach for unlabelled point-sets and sub-manifolds matching

In the paper, we study the problem of optimal matching of two generalized functions (distributions) via a diffeomorphic transformation of the ambient space. In the particular case of discrete distributions (weighted sums of Dirac measures), we provide a new algorithm to compare two arbitrary unlabelled sets of points, and show that it behaves properly in limit of continuous distributions on sub-manifolds. As a consequence, the algorithm may apply to various matching problems, such as curve or surface matching (via a sub-sampling), or mixings of landmark and curve data. As the solution forbids high energy solutions, it is also robust towards addition of noise and the technique can be used for nonlinear projection of datasets. We present 2D and 3D experiments.
Predicting Patient Satisfaction With Ensemble Methods

Health plans are constantly seeking ways to assess and improve the quality of patient experience in various ambulatory and institutional settings. Standardized surveys are a common tool used to gather data about patient experience, and a useful measurement taken from these surveys is known as the Net Promoter Score (NPS). This score represents the extent to which a patient would, or would not, recommend his or her physician on a scale from 0 to 10, where 0 corresponds to “Extremely unlikely” and 10 to “Extremely likely”. A large national health plan utilized automated calls to distribute such a survey to its members and was interested in understanding what factors contributed to a patient’s satisfaction. Additionally, they were interested in whether or not NPS could be predicted using responses from other questions on the survey, along with demographic data. When the distribution of various predictors was compared between the less satisfied and highly satisfied members, there was significant overlap, indicating that not even the Bayes Classifier could successfully differentiate between these members. Moreover, the highly imbalanced proportion of NPS responses resulted in initial poor prediction accuracy. Thus, due to the nonlinear structure of the data, and high number of categorical predictors, we have leveraged flexible methods, such as decision trees, bagging, and random forests, for modeling and prediction. We further altered the prediction step in the random forest algorithm in order to account for the imbalanced structure of the data.
GENETIC PROGRAMMING WITH CROSS-TASK KNOWLEDGE SHARING FOR LEARNING OF VISUAL CONCEPTS

This thesis concentrates in the fields of computer vision, image understanding and machine learning. We present a novel approach for learning visual concepts from raw image data. Our method is able to learn new concepts and, in result, acquire knowledge that can be than used in standard computer vision tasks such as pattern recognition, object identification or object tracking. The acquired knowledge is encoded in a form of individuals/learners which are able to process visual information. The originality of this approach lies also in the fact that individuals do not process raw image data directly. Instead, they operate on a set of attributed visual primitives that are acquired from images in the preliminary stage of the processing. The proposed approach is very general, because it uses the learning principle. For the purpose of learning we use genetic programming. Each individual/learner represents a procedure in a form of a tree of operations on sets of visual primitives. The images from the training set in our approach are not labeled in any way and no information is assigned with them. It means that the knowledge is acquired in totally unsupervised way. The only feedback information that states which individual better represents the target concept from the training set is the measure how the individual is able to reconstruct the input image. The rational standing behind this approach is following. We believe that a visual concept can be proved to be acquired and understood only if it can be reconstructed and successfully compared with original image from it was previously learned. In the thesis we present our methodology in details and describe its exemplary implementation that is based on the concept of segment as visual primitive. The method is verified on several sets of shapes of simple objects such as triangles, sections, Y letters and others. We also elaborate a methodology of cross-task knowledge sharing that is a step towards modularization of knowledge and provide results of massive experiments with cross-task knowledge sharing. Finally, the most important design decisions of software environment that was developed as a platform for computational experiments with our approach are described. A particular emphasis is put on computational performance issues of the developed software.
Using genetic algorithms to develop scoring models for alternative measures of performance

Most approaches to credit scoring generate model parameters by minimising some function of individual error, or by maximising likelihood. In practice, the criteria by which the parameters of a model are determined and the criteria by which models are assessed may differ. Practitioners tend not to be interested in standard measures such as the R2 coefficient for linear regression or the likelihood ratio for logistic regression. Instead, performance will be assessed using global measures such as the GINI coefficient, or by considering the misclassification rate at different points in the distribution of model scores. In this paper an approach using genetic algorithms is described, where the training algorithm is used to directly maximise/minimise the performance measure of interest. Empirical results are presented, showing that genetic algorithms have the potential to generate scoring models that are competitive with models constructed using more traditional approaches, and that there is scope for improved models when prior information about model usage is incorporated within the parameter estimation process.