Miracle-dz/UniFAR-FTUs huggingface dataset:
Cleft extensions for quasi-entwining structures

Abstract In this paper we introduce the notions of quasi-entwining structure and cleft extension for a quasi-entwining structure. We prove that if (A, C, ψ) is a quasi-entwining structure and the associated extension to the submagma of coinvariants AC is cleft, there exists an isomorphism ωA between AC ⊗ C and A. Moreover, we define two unital but not necessarily associative products on AC ⊗ C. For these structures we obtain the necessary and sufficient conditions to assure that ωA is a magma isomorphism, giving some examples fulfilling these conditions.
On the optimal stopping time of learning

The goal of this thesis is to study the economics of computational learning. Attention is also paid to applications of computational learning models, especially Valiant's so-called `probably approximately correctly' (PAC) learning model, in econometric situations. Specifically, an economically reasonable stopping time model of learning is the subject of two attached papers. In the rst paper, Paper A, the economics of PAC learning are considered. It is shown how a general form of the optimal stopping time bounds can be achieved using the PAC convergence rates for a `pessimistic-rational' learner in the most standard binary case of passive supervised PAC model of finite Vapnik-Chervonenkis (VC) dimension. The second paper, Paper B, states precisely and improves the ideas introduced in Paper A and tests them in a specific and mathematically simple case. Using the maxmin procedure of Gilboa and Schmeidler the bounds for the stopping time are expressed in terms of the largest expected error of recall, and thus, effectively, in terms of the least expected reward. The problem of locating a real number θ by testing whether xi ≤ θ , with xi drawn from an calculated for a range of term rates, sample costs and rewards/penalties from a recall ae included. The standard econometric situations, such as product promotion, market research, credit risk assessment, and bargaining and tenders, where such bounds could be of interest, are pointed. These two papers are the essence of this thesis, and form it togheter with an introduction to the subject of learning.
Economic Statistics: How to Become Lean and Mean?

Abstract The compilation of official statistics has changed dramatically over the past decades, and it will continue to change. These changes not only relate to user demands; also the source data and the means, in the sense of the technologies available for compiling statistics, have undergone and will continue to undergo significant changes. This article addresses the main developments and challenges in relation to the sources for compiling official statistics (input), the technologies for processing data (throughput), and the user demands (output) for official statistics. It is concluded that all these changes have, and continue to have, a significant impact on the way statistics are compiled, and the article puts forward a number of suggestions for the future direction of compiling official statistics.
Modeling Analysis of Complex Products Resource Integration Behavior Under Distributed Collaborative Technology Innovation Mode

Abstract According to new features of distributed technology innovation collaboration of complex products of China’s aviation, in the multi-agent collaborative R&D process, between main manufacturer and suppliers, this paper summarizes the organizational structure and characteristics of collaborative mode of distributed technology innovation of complex products, basing on the technological innovation and resource integration of complex products, and treating resource contribution rate as a key parameter. It establishes multi-agent resource integration collaborative symbiosis model under distributed collaborative technology innovation mode based on symbiosis theory. And finds that some factors such as the basic attributes of the enterprise, the mutual influence of resources among enterprises and the behavior decision of the main manufacturer have some effects on the resource integration using stability analysis and simulation analysis, which provides a theoretical basis for the main manufacturer to optimize the behavior of suppliers’ resource integration.
An EEG spike detection algorithm using artificial neural network with multi-channel correlation

An automatic spike detection algorithm for classification of multi-channel EEG signals based on artificial neural network is presented. Radial basis function (RBF) neural network was chosen for single channel recognition, with model optimization using receiver operating characteristics analysis. Waveform simplification was employed for high noise immunity. Feature extraction with as few as three parameters was used as preparation for the inputs to the neural network. Identification of multi-channel geometric correlation was performed to further lower the false-positive rate by using an incidence matrix. Threshold value for spike classification was chosen for simultaneous maximization of detection sensitivity and selectivity. Evaluation with visual analysis in this preliminary study showed a 83% sensitivity using 16-channel continuous EEG records of four patients, while a high false positive rate was found, which was believed to arise from the extensive and exhaustive visual analysis process. The computation time required for spike detection was significantly less than that needed for online display of the signals on the monitor. We believe that the algorithm proposed in this study is robust and that the simple structure of RBF neural network yields high potential for real-time implementation.
Resolving abstract anaphors in Spanish discourse: Underspecification and mereological structures

Abstract Anaphoric underspecification involves multiple potential candidate antecedents for an anaphoric expression. In abstract object anaphora, where linguistic antecedents are clauses, sentences and larger fragments of discourse, the source of referential underspecification is commonly found at the propositional level. Thus, underspecified abstract anaphors have multiple antecedents of a higher-order nature (i.e., propositions and events). Following previous research on anaphoric underspecification with nominal antecedents, I propose a hypothetical three-step process toward the resolution of underspecified abstract object anaphors by hearers in discourse: (i) creation of a complex abstract object with a mereological structure that includes all potential interpretations for an anaphor, (ii) recognition of the thematic connection among propositions intended by the speaker in the form of a specific rhetorical relation, and 3) resolution of the abstract anaphor. Potential antecedents for any underspecified abstract anaphor may include atomic propositions and complex abstract referents that result from a merged interpretation of several propositions that are thematically connected. Provided that it is available, I claim that such a merged interpretation, which is part of the mereological structure, is the preferred interpretation as it is generally interpreted as part of a general purpose by the speaker, in addition to contributing to the thematic coherence of discourse.
Der Einfluss von Fortbewegung auf die sensorische Informationsverarbeitung und die zugrunde liegenden neuronalen Schaltkreise

Zusammenfassung Die Verarbeitung sensorischer Information kann sowohl im Kortex als auch im Thalamus durch den Verhaltenskontext, z. B. durch aktive Fortbewegung, moduliert werden. Solch aktives Verhalten verbessert die Kodierung sensorischer Reize und die Wahrnehmung, besonders während Aktivitäten von moderater Intensität. Der Modulation sensorischer Verarbeitung durch Fortbewegung scheint eine Kombination von Mechanismen zugrunde zu liegen, unter anderem neuromodulatorische Einflüsse, die Aktivität spezifischer, inhibitorischer Interneurone, sowie top-down- oder motorische Rückprojektionen. Neue experimentelle Ansätze, die es Mäusen erlauben, sich trotz Kopffixation auf Laufbällen oder –bändern fortzubewegen, ermöglichte es in den letzten Jahren, die neuronalen Schaltkreise und zellulären Elemente, die der Modulation durch Verhaltenskontext zugrunde liegen, eingehend zu untersuchen. Dieser Übersichtsartikel fasst den momentanen Stand dieser Studien zusammen und beleuchtet wichtige offenen Fragen.
Students’ Total Experience within a Romanian Public University

This paper focuses on the understanding of experience marketing in a higher education context and also on its implications on students’ loyalty. Therefore, the article explores the most significant determinants of students’ experience, such teaching process, administrative service, courses content, library, accommodation, eating spaces and medical services, university’s space and campus’s facilities, university’s reputation and the provided career prospects. The research aims to outline the importance of analysing students’ experience in the increasingly competitive market of educational services and also to provide a sustainable basis for the Romanian higher education improvement. In its last section, the objective of the research is to measure the student’s loyalty towards the university and also the experience’s impact on their loyalty. The originality of this study consist in the authors attempt to design a student’s total experience survey, this paper being between the first ones analysing this topic in Romania.
An efficient method for alleviating line overloads and voltage violations by corrective active and reactive rescheduling

This paper describes simple and efficient method for power system overloads and voltage limit violations alleviation. The advantages of active power-bus angle and reactive power-voltage magnitude decoupling principles to obtain savings in computational time and storage are considered. The decomposed approach involves the solution of an optimization problem without constraints followed by the solution of a corrective active and reactive power rescheduling in a separate and sequential form. The proposed algorithm gives satisfactory results in the alleviation of overloads and voltage limit violations when tested on two sample test power systems.
Collaboration of Two Service-Learning Courses: Software Development and Technical Communication

We describe a service-learning collaboration between a software development course and a technical communication course. Good documentation is essential for easing users’ experience with a software system, as well as for introducing maintenance and support personnel to the system. Therefore, one of the main goals of this collaboration was to produce usable documentation to accompany the studentdeveloped software. Additionally, the goals were to provide students with a hands-on learning experience and to provide more time for software development. We discuss the courses involved, the goals/benefits of the collaboration, the collaboration process, the method of student communication, predicted challenges, assessment criteria, and results and future improvements. Despite the challenges with this first attempt at the collaboration, the results were encouraging and we have gained invaluable insight into how to improve for the future.
Activities of the R/V MIRAI Nauru99 cruise and its early results

The key factor of the international field experiment Nauru99 was to provide various data sets for the intercomparison with the Atmosphaic Radiation and Cloud Station (ARCS) on Nauru Island deployed by the Atmospheric Radiation Measurement (ARM) program of the U.S. Department of Energy (DOE) and it was conducted as a collaborative research campaign of three agencies; DOE, the U.S. NOAA and the Japan Marine Science and Technology Center (JAMSTEC). The Japanese research vessel (R/V) MIRAI participated in the campaign and conducted a stationary observation from 17 June through 4 July 1999 (IOP-Intensive Observing Period). The ARCS site, the NOAA's R/V Ronald H. Brown (RHB) and the MIRAI served as platforms with the ATLAS buoys deployed along the 165/spl deg/E and the Cessna operated by Flinders University, Australia. Since convection is one of the key elements affecting the radiative balance over the warm water pool region in the tropical western Pacific Ocean, the distribution of convections developed during the IOP is studied using scanning radar reflectivity data.
Tetraaqua(2,6‐dioxo‐1,2,3,6‐tetrahydropyrimidine‐4‐carboxylato‐κ2N3,O)cobalt(II) 2.5‐hydrate

The CoII ion in the title compound, [Co(C5H2N2O4)(H2O)4]·2.5H2O, is chelated by the orotate dianion through carboxyl­ate O and pyrimidine N atoms, and its octa­hedral geometry is completed by four water mol­ecules. An inter­molecular N—H⋯O hydrogen bond forms an orotate–orotate dimer. An extensive three-dimensional hydrogen-bonded network of O—H⋯O and N—H⋯O bonds and weak π–π inter­action [3.754 (2) A] stabilize the crystal structure. One of the solvent water mol­ecules is located on a twofold rotation axis.
Design of Human Facial Feature Recognition System

Augmenting human computer interaction with automated analysis and synthesis of facial expressions is the goal towards which much research effort has been devoted to in the last few years. Facial feature recognition is one of the important aspects of natural human-machine interfaces; it has great applications such as in behavioral science, security systems and in clinical practice. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine is still a challenging task. The face expression recognition problem is challenging because different individuals display the same expression differently. In this project we are trying to design a facial feature recognition system in real time using the concepts of Haar classifiers, contour concepts, template matching and studying some models related to it. We have tried first to extract face region from the video using above mentioned approach and had tried to extract some facial features and locate their position in the image.
Electrocardiographic changes following six months of long-distance triathlon training in previously recreationally active individuals

ABSTRACT Background: Clinical electrocardiographic (ECG) guidelines for athlete’s heart are based upon cross-sectional data. We aimed to longitudinally evaluate the influence of endurance training on the ECG and compare the prevalence of ECG abnormalities defined by contemporary criteria. Methods: A group of 66 training-naïve individuals completed a six-month training programme with resting ECGs and cardiopulmonary exercise tests performed at baseline, two and six months. Data were analysed using repeated measures analysis of variance and the prevalence of ECG abnormalities compared between proposed criteria. Results : Maximal oxygen consumption increased from 45.4 ± 7.1 to 50.3 ± 7.1 ml·kg−1·min−1 (p < 0.05) pre-to-post training. ECG changes included, bradycardia (60 ± 12 vs. 53 ± 8 beats·min−1; p < 0.05), shorter P wave duration (106 ± 10 vs. 103 ± 11 ms; p < 0.05), reduced QTc (413 ± 27 vs. 405 ± 22 ms; p < 0.05), and increased left ventricular Sokolow-Lyon index (2.45 ± 0.66 vs. 2.62 ± 0.78 mV; p < 0.05). 85% of individuals showed ≥1 ‘training-related’ ECG finding at six months vs. 68% at baseline. Using the 2013 Seattle Criteria, 4 ECGs were ‘abnormal’ at baseline and 3 at month six vs. 2 at baseline and 1 at month six, using the 2017 International Consensus. Prevalence of ‘borderline’ findings did not increase with training (11% at baseline and six months). Conclusion: Six-months endurance training leads to a greater prevalence of ‘training-related’ but not ‘borderline’ or ‘training-unrelated’ ECGs. ‘Borderline findings’ may not necessarily represent training-related cardiac remodelling in novice athletes following a six-month training intervention. KEY MESSAGES This study aimed to assess the longitudinal ECG changes following six months of endurance training, in training-naïve individuals, and whether these ECG changes support the revisions made to the recent 2017 international consensus criteria. The prevalence of ‘training-related’ findings were increased with six months of endurance training, however the prevalence of the revised ‘borderline’ criteria, according to the 2017 international consensus, did not increase and the associated quantitative ECG data (e.g. P-wave amplitude, QRS axis and QRS duration) remained unchanged. Further clinical consideration may be warranted for individuals within the early phase of exercise engagement presenting with ‘borderline’ ECG abnormalities, defined by the International criteria.
Understanding Usability Work as a Human Activity

Usability evaluation is widely accepted as a valuable activity in software development. However, how results eff ectively are fed back to developers is still a relatively unexplored area. We argue that usability feedback can be understood as an argument for a series of usability problems, and that basic concepts from argumentation theory can help us understand how to create persuasive feedback. We revisit two field studies on usability feedback to study if concepts from Toulmin’s model for argumentation and Aristotle’s modes of persuasion can explain why some feedback formats outperform others. We recommend that evaluators specifically back up the warrants behind their usability claims, that their arguments use several modes of persuasion, and that they present feedback in browsable amounts not to overwhelm developers with information. For complex and controversial problems, we advise evaluators to involve developers in a learning process and provide the opportunity to experience and discuss the findings.
A Detection Method for GLUT4 Exocytosis Based on Spontaneous Split Luciferase Complementation

Glucose transporter 4 (GLUT4) is an insulin-regulated glucose transporter, which is vital for blood glucose homeostasis. To clarify the physiological roles of GLUT4, quantitative measurement of GLUT4 exocytosis is indispensable. Herein, we show a rapid detection system for GLUT4 on the cell surface using spontaneous split-luciferase reconstitution. Upon insulin-induced GLUT4 exocytosis, GLUT4 was exposed outside, where luciferase is reconstituted and emitted luminescence. Pretreatment with inhibitors reduced the insulin-induced signal elevation. The results indicate that the developed method is applicable to high-throughput analysis on GLUT4 trafficking, which will greatly accelerate comprehensive research on the physiological roles of GLUT4.
Obviation of Secluded Information on Social Networks from Inference Assails

this is the problem. So in this we explore how to overcome this problem by launching inference attacks using released social network data to predict confidential private information about individuals. We then arrange some possible sanitization techniques that could be used in various situations. Then, we explore the effectiveness of these techniques by studying Social network analysis, data mining, social network privacy and implementing them on a data set. We show that we can decrease the effectiveness of both local and relational classification algorithms by using the sanitization methods we described.
Sparse Learning Package with Stability Selection and Application to Alzheimer's Disease

Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and overlapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence i guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
Feedforward neural networks with random weights

In the field of neural network research a number of experiments described seem to be in contradiction with the classical pattern recognition or statistical estimation theory. The authors attempt to give some experimental understanding why this could be possible by showing that a large fraction of the parameters (the weights of neural networks) are of less importance and do not need to be measured with high accuracy. The remaining part is capable to implement the desired classifier and because this is only a small fraction of the total number of weights, the reported experiments seem to be more realistic from a classical point of view. <<ETX>>