Miracle-dz/UniFAR-FTUs huggingface dataset:
Broken rotor bar fault detection in induction motors using starting current analysis

Fault detection based on a common steady-state analysis technique, such as FFT, is known to be significantly dependant on the loading conditions of induction motors. At light load, it is difficult to distinguish between healthy and faulty rotors because the characteristic broken rotor bar fault frequencies are very close to the fundamental component and their amplitudes are small in comparison. As a result, detection of the fault and classification of the fault severity under light load is almost impossible. In order to overcome this problem, this paper investigates the detection of rotor faults in induction machines by analysing the starting current using a newly developed quantification technique based on the wavelet transform. The analysis technique applies the wavelet transform to the envelope of the starting current. The envelope extraction is used to remove the strong fundamental component, which overshadows the characteristic differences between a healthy motor and a faulty motor with broken rotor bars. The results are then verified using tests on a machine with a varying numbers of broken bars. The effects of initial rotor position, supply imbalance and loading are also investigated
Neural correlates of verbal fluency and associations with demographic, mood, cognitive and tumour factors in brain tumour patients.

Verbal fluency tests are one of the most commonly used measures of executive functioning in neuropsychological testing and play an important role in the assessment, diagnosis and care planning of patients with a variety of conditions, including brain tumour. There is little conclusive evidence about which factors may influence verbal fluency outcomes. No studies to date have investigated the interactions between a comprehensive range of demographic variables, mood scores, tumour factors and key cognitive skills with the focus of verbal fluency outcomes in brain tumour patients. Similarly, clarification is required across studies assessing the localisation effects of verbal fluency skills. To address these gaps in the evidence base this study used a retrospective cohort design of cross-sectional data from patients with brain tumours, to investigate their performance of both phonemic and semantic verbal fluency. More specifically this study used simple linear and multiple regression calculations to analyse the interactions between these variables and other potentially important factors such as localisation, depression and anxiety (using the HADS), age, gender, education, premorbid functioning (using the TOPF), semantic memory (using the GNT), and tumour type. The results showed that an increase in phonemic fluency performance was significantly correlated with being educated, an increase in semantic memory, and an increase in premorbid functioning. Phonemic fluency was also significantly correlated with localisation. In general, a decrease in phonemic fluency was significantly associated with tumours in the left frontal lobe. An increase in semantic fluency was correlated with an increase in semantic memory. No other factors showed significant associations with phonemic or semantic fluency. The outcomes from the hierarchical multiple regressions indicated that localisation, gender, education, tumour type, depression, semantic memory, and premorbid functioning when combined can predict phonemic fluency variance. Combining localisation effects, semantic memory, depression and education together do not result in a model that predicts semantic fluency, as within this model the only significant relationship was between semantic memory and semantic fluency. These findings show that, for brain tumour patients, it is important to take into consideration tumour localisation, education, semantic memory, and premorbid functioning when assessing and care planning for deteriorations in phonemic fluency. Similar patients with deteriorations in semantic fluency need to have their results considered in light of performance in semantic memory tests.
Discriminative Bayesian Dictionary Learning for Classification

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.
Stator flux orientation control of induction machines in the field weakening region

The behavior of a director stator-flux-oriented (SFO) system in the field weakening region is studied and compared with that of a direct rotor-flux-oriented (RFO) system. The steady-state analysis shows that the SFO system always retains maximum torque capability, not matter how large the error in the parameters. In contrast, RFO is sensitive to variations in leakage and magnetizing inductances. When detuned, RFO yields reduced torque capability and poorer torque/ampere ratio, or even static instability. Digital simulation also shows that SFO can have dynamic response comparable to tuned RFO. <<ETX>>
× U ( 1 ) symmetry of the Kimura 3 ST model and phylogenetic branching processes

An analysis of the Kimura 3ST model of DNA sequence evolution is given on the basis of its continuous Lie symmetries. The rate matrix commutes with a U(1)×U(1)×U(1) phase subgroup of the group GL(4) of 4× 4 invertible complex matrices acting on a linear space spanned by the 4 nucleic acid base letters. The diagonal ‘branching operator’ representing speciation is defined, and shown to intertwine the U(1)×U(1)×U(1) action. Using the intertwining property, a general formula for the probability density on the leaves of a binary tree under the Kimura model is derived, which is shown to be equivalent to established phylogenetic spectral transform methods.
Using semantic features to improve large-scale visual concept detection

Currently there are many multimedia benchmarks and databases available with a predefined set of concepts for which detectors can be formed or are even already available. One can use these background concepts to form semantic concept vectors for each image or video in the database by concatenating the concept prediction outputs. In this paper we investigate the use of such semantic concept features for detecting novel concepts in two large-scale experiments: the TRECVID 2012 evaluation with 800 hours of video data, and MIRFLICKR with 1 million images. We show that the detection performance can improve significantly over using visual features only. In some applications, computationally expensive kernel classifiers cannot be used in the detection phase, and our experiments show a consistent significant improvement using fast linear classifiers when we replace visual features with the semantic concept feature. We also propose a Self-Organising Map-based method which affords fast training-free detection and intuitive visualisation properties.
New Challenges : Large Scale Automatic Semantic Integration

Today schema matching is a basic problem in almost every data intensive distributed application, namely enterprise information integration, collaborating web services, ontology based agents communication, web catalogue integration and schema based P2P database systems. There has been a plethora of algorithms and techniques researched in schema matching and integration for data interoperability. Numerous surveys have been presented in the past to summerize this research. The requirement for extending the previous surveys has been created because of the mushrooming of the dynamic nature of these data intensive applications. Today data is viewed as a semantic entity, motivating new algorithms and strategies. The evolving large scale distributed information systems are further pushing the schema matching research to utilize the processing power not available in the past. Thus directly increasing the industry investment proportion in the matching domain. This article reviews the latest application domains in which schema matching is being utilized. It discusses the shift from manual to automatic schema matching, along with new definition of match covering the automation objectivity. Another panorama which is covered by this survey is the schema matching classification in the context of latest strategies and algorithms in the field of schema based information retrieval and management.
Social Security Programs and Retirement around the World: Fiscal Implications of Reform

agers and regulators. In the opinion of this reviewer, academics and scholars of probability, statistics, and financial mathematics will likely be familiar with many of the ‘‘model weaknesses’’ described in this book. Moreover, I found myself nodding more often than expressing shock or dismay at the arguments being made. The intellectual and historical factoids on statistics, probability, and risk-theory were interesting, though mostly known to probability researchers. Arguably everyone teaching basic financial risk management from a quantitative perspective (in a business school or economics department) should be aware of the limitations of these mathematical models and add disclaimers. That said, I found appealing the ways in which the ideas were presented by this well-regarded practitioner and respected scholar. I especially commend the author for not taking a sledgehammer to the entire risk-management ‘‘quant’’ industry and instead offering a thought-provoking and helpful guide through the good, the bad, and the ugly. This book should be on the reading list of experienced risk managers in the financial services industry as well as students who are contemplating a career in the field. It provides a thoughtful qualitative companion to more equation-laden texts on modern risk management.
Robotic Pose Estimation via an Adaptive Kalman Filter Using State-varying Noise

Swarm robotics offers the promise of enhanced performance and robustness relative to that of individual robots, with decreased cost or time-to-completion for certain tasks. Having many degrees of freedom, the swarm related control and estimation problems are quite challenging, specifically when the solutions involve a large amount of communication among the robots. Under certain sensing modalities, direct measurement of robot orientation may either be not possible or require excessive sensor processing. In this paper, a novel method is presented to vary process noise intensity as a function of an estimated state in order to arrive at the hidden state robot orientation. Experimental results are provided, demonstrating the efficacy of the method as well as the error reduction relative to fixed-noise estimation.
Using Extreme Programming for Knowledge Transfer

This paper presents the application of eXtreme Programming to the software agents research and development group at TRLabs Regina. The group had difficulties maintaining its identity due to a very rapid turnover and lack of strategic polarization. The application of eXtreme Programming resulted in a complete reorientation of the development culture, which now forms a continuous substrate in every individual. This allowed the creation of a group identity, thus promoting the integration of short-term projects in the strategic group vision, and complete information sharing.
Natural Capital, Subjective Well-Being, and the New Welfare Economics of Sustainability: Some Evidence from Cross-Country Regressions

The measurement of natural capital and its management during the economic development process are important aspects of the capital approach to sustainable development. However, the assessment of social welfare in terms of genuine savings (or changes in total wealth per capita) is arguably too limited. This paper tries to make a case for the incorporation of subjective well-being measures in debates about sustainable development by exploring the macro-level relationship between subjective well-being and natural capital in a cross-country setting. It is tested whether natural capital per capita is correlated with subjective well-being in a sample of fifty-eight developed and developing countries, using natural capital data from the World Bank's Millennium Capital Assessment. Bivariate regressions indicate that it is. When multiple regression models are estimated that include (a) major country-level determinants of subjective well-being (GNI per capita, social capital, income distribution, unemployment, inflation), and (b) regional dummy variables for ex-Soviet Union and Latin American countries, the positive correlation remains. The role of data outliers is carefully explored, and the sensitivity of the results to the use of alternative subjective well-being measures (i.e. life satisfaction, happiness, and a combined life satisfaction and happiness index) is investigated. This does not change the nature of the results. The findings arguably strengthen the case for a 'new welfare economics of sustainability' that takes subjective well-being measures into account.
A Viewpoint on: How Xenopus laevis embryos replicate reliably: Investigating the random-completion problem

, 041917 (2008) – Published October 27, 2008Complete and timely replication of the genome is aprerequisite to fulﬁlling the “dream” of every cell to be-come two cells [1]. So far, biologists have been success-ful in identifying the processes involved in DNA repli-cation, but they have not been able to explain a fun-damental control problem that cells face, the “random-completion” or “random-gap” problem: how do cellsensure that every last piece of the genome is replicatedon time [2]? In a paper in Physical Review E, Scott C. -H.Yang and John Bechhoefer of Simon Fraser Universityuse insights from condensed-matter physics to answerthis question [3]. Using a physical model originally de-veloped to describe the kinetics of ﬁrst-order phase tran-sitions, they show that, despite the intrinsic stochasticityof the initiation of DNA replication, cells can still con-trol the amount of time it takes to replicate the genome.The authors thus provide a rigorous solution to a long-standing problem in cell biology. The elegance of theirformal approach bridging physics and biology, and thedepth of their analysis, should inspire scientists fromboth disciplines.The heart of the problem is that the sites at whichreplication initiates are randomly distributed along thechromosomes of Xenopus laevis embryos, a frog widelyused in cell biology experiments. There are on the or-der of 10
The Relationship Between Resting Heart Rate Variability, Hostility, and In Vivo Aggression Among Young Adults

ABSTRACT Hostility, as well as its behavioral manifestation (i.e., aggression), are associated with significant health problems and psychological distress. Yet, there is only limited research examining how resting autonomic nervous system activity, indexed by Heart Rate Variability (HRV), is associated with these constructs. Specifically, dampened high frequency (HF)-HRV and heightened low frequency (LF)/HF-HRV are associated with increased trait hostility and aggression, while dampened heart rate (HR) is only associated with increased trait aggression. Collectively, this suggests that dampened physiological regulation may be associated with trait hostility and aggression. However, no study has examined how resting HRV is associated with in vivo aggression or uniquely associated with trait hostility. The present study examined the relationship between hostility, anger, and aggression with resting HRV in 81-undergraduate students (55 women, mean age = 22). Trait measures were assessed using self-report questionnaires and in vivo aggression was assessed using the Taylor Aggression Paradigm. Results found that HF-HRV and LF-HRV were negatively associated with trait hostility, while only LF-HRV uniquely predicted trait hostility. Further, HR was negatively associated with unprovoked aggression, while LF/HF-HRV was positively associated with provoked aggression and greater increases in aggression when provocation increased. These findings suggest that (at least in our sample) dampened resting cardiovascular flexibility (HF-HRV and LF-HRV) may be associated with a disposition toward distrust and cynicism among young adults. Further, under-arousal (low HR) may be associated with aggression when there is no clear provocation, while heightened autonomic balance (LF/HF-HRV) may be associated with aggression in response to provocation among young adults.
Limits of Data-Level Parallelism

Abstract—A new breed of processors like the Cell Broadband Engine, the Imagine stream processor and the various GPU processors emphasize data-level parallelism (DLP) and threadlevel parallelism (TLP) as opposed to traditional instructionlevel parallelism (ILP). This allows them to achieve order-ofmagnitude improvements over conventional superscalar processors for many workloads. However, it is unclear as to how much parallelism of these types exists in current programs. Most earlier studies have largely concentrated on the amount of ILP in a program, without differentiating DLP or TLP. In this study, we investigate the extent of data-level parallelism available in programs in the MediaBench suite. By packing instructions in a SIMD fashion, we observe reductions of up to 91 % (84 % on average) in the number of dynamic instructions, indicating a very high degree of DLP in several applications. I.
Improving value-at-risk estimates by combining kernel estimation

In this paper we develop an improvement on one of the more popular methods for Value-at-Risk measurement, the historical simulation approach. The procedure we employ is the following: First, the density of the return on a portfolio is estimated using a non-parametric method, called a Gaussian kernel. Second, we derive an expression for the density of any order statistic of the return distribution. Finally, because the density is not analytic, we employ Gauss-Legendre integration to obtain the moments of the density of the order statistic, the mean being our Value-at-Risk estimate, and the standard deviation providing us with the ability to construct a confidence interval around the estimate. We apply this method to trading portfolios provided by a financial institution.
Informational approaches to regulation

How does risk labeling information on hazardous household chemicals and pesticides influence consumer behavior? While many studies speculate on the effects of risk information, Magat and Viscusi draw on a series of extensive surveys to assess the likely response. Their set of original studies of household chemicals, energy audits, and food risk labeling establishes guidelines for the design and evaluation of these informational regulations. Their findings also include new estimates of the valuation of nonfatal health risks, the first estimates in the literature of the role of altruism, and an assessment of the influence of irrational responses to risk. Although economists suggest that giving consumers information about potentially hazardous goods is preferable to direct regulation of product content, implementation of information regulation raises a host of issues that need to be addressed. Magat and Viscusi document the cognitive limitations that consumers have in processing information and break new ground by showing how, given this behavior, the informational regulations should be designed. Case studies assess the degree to which different kinds of consumers notice, remember, and heed printed warnings in a range of wordings and formats. They then examine risk valuation, showing how much consumers are willing to pay for increased product safety under various conditions. A concluding chapter synthesizes the results and discusses their implications for regulatory policy.
High post‐movement parietal low‐beta power during rhythmic tapping facilitates performance in a stop task

Voluntary movements are followed by a post‐movement electroencephalography (EEG) beta rebound, which increases with practice and confidence in a task. We hypothesized that greater beta modulation reflects less load on cognitive resources and may thus be associated with faster reactions to new stimuli. EEG was recorded in 17 healthy subjects during rhythmically paced index finger tapping. In a STOP condition, participants had to interrupt the upcoming tap in response to an auditory cue, which was timed such that stopping was successful only in ~ 50% of all trials. In a second condition, participants carried on tapping twice after the stop signal (CONTINUE condition). Thus the conditions were distinct in whether abrupt stopping was required as a second task. Modulation of 12–20 Hz power over motor and parietal areas developed with time on each trial and more so in the CONTINUE condition. Reduced modulation in the STOP condition went along with reduced negative mean asynchronies suggesting less confident anticipation of the timing of the next tap. Yet participants were more likely to stop when beta modulation prior to the stop cue was more pronounced. In the STOP condition, expectancy of the stop signal may have increased cognitive load during movement execution given that the task might have to be stopped abruptly. However, within this condition, stopping ability was increased if the preceding tap was followed by a relatively larger beta increase. Significant, albeit weak, correlations confirmed that increased post‐movement beta power was associated with faster reactions to new stimuli, consistent with reduced cognitive load.
End User Developable Informative Art ( EUDIA ) : Demonstration of Simple Proof of Concept

— In the last decade we have witnessed a rapid development of various display technologies, as well as a rapid lowering down of prices of existing display technologies. These developments have opened up the new opportunities to deploy computer graphics in our day to day activities. One of these interesting deployments of computer graphics is " Informative Art " , which presents information in aesthetic way without demanding attention from the users. The currently developed Informative Art applications lacks the end user tailorability. In this research article we would like to propose the application of End User Development (EUD) in the field of Informative Art.