Miracle-dz/UniFAR-FTUs huggingface dataset:
Par-delà les frontières disciplinaires et linguistiques: l'influence des penseurs français sur la recherche en comptabilité

Since the mid-1980s, a number of accounting researchers have published research based on the work of French intellectuals like Michel Foucault. This article deals with the influence on accounting research exercised by the writings of French intellectuals. In particular, we seek to document this influence and to bring to light certain factors having contributed to its spread. Among these factors, one may count the opening up of the journal Accounting Organizations and Society to foucauldian research, the first Interdisciplinary Perspectives on Accounting (IPA) Conference held at the University of Manchester in 1985, and the doctoral training acquired by several British accounting researchers in disciplines outside of accountancy like sociology or history.
L'urbanisation indienne (1901-1981)

Résumé Vêron Jacques. - L'urbanisation indienne (1901-1981). Du début du siècle à aujourd'hui, le taux d'urbanisation de l'Inde progresse lentement (de 11 % a 23 %) mais le nombre de citadins sextuple (26 millions en 1901 et 126 en 1981). Les structures urbaines des différents états sont contrastées selon les états, la plus grande agglomération rassemble de 8% (Madhya Pradesh) à 64% (Bengale occidentale) de la population urbaine Un des phénomènes majeurs est la croissance régulière du poids des villes de plus de 100 000 habitants (26 % des citadins y vivent au début du siècle, 61 % aujourd'hui). Le nombre de ces villes augmente à chaque recensement comme leur taille moyenne La croissance des villes de plus de 100 000 habitants est indépendante de leur âge (durée d'appartenance à cette classe de ville) et de leur taille La localisation géographique n'explique pas, a elle seule, les disparités de rythme de croissance des grandes villes Le croisement des critères de localisation géographique et de taille ne permet pas non plus de déceler un ordre. L'histoire propre de chaque ville est dominante.
Robust regularized least-squares beamforming approach to signal estimation

In this paper, we address the problem of robust adaptive beamforming of signals received by a linear array. The challenge associated with the beamforming problem is twofold. Firstly, the process requires the inversion of the usually ill-conditioned covariance matrix of the received signals. Secondly, the steering vector pertaining to the direction of arrival of the signal of interest is not known precisely. To tackle these two challenges, the standard capon beamformer is manipulated to a form where the beamformer output is obtained as a scaled version of the inner product of two vectors. The two vectors are linearly related to the steering vector and the received signal snapshot, respectively. The linear operator, in both cases, is the square root of the covariance matrix. A regularized least-squares (RLS) approach is proposed to estimate these two vectors and to provide robustness without exploiting prior information. Simulation results show that the RLS beamformer using the proposed regularization algorithm outperforms state-of-the-art beamforming algorithms, as well as another RLS beamformers using a standard regularization approaches.
Load sharing characteristic of single phase PV inverter connected to grid

This paper describes model and simulation of single phase PV inverters that work independently to supply three phase system as a part of renewable distributed generation. Load sharing characteristic of PV inverter and grid during supplying local load with current control method and relation of PV power level and current control performance to load sharing are simulated and analyzed. Furthermore, power quality behavior during connection/disconnection of PV plant, grid harmonics, voltage and current unbalance are simulated and analyzed. Model and simulation are implemented using MATLAB/Simulink.
Acute Myocardial Infarction in a Young Lady due to Vitamin B12 Deficiency Induced Hyperhomocysteinemia

Hyper-homocysteinemia is a risk factor for coronary artery disease in young patients. A 32 years old female without any conventional risk factors except obesity presented with acute anterior wall myocardial infarction (MI). Her echocardiography showed anterior wall hypokinesia with moderate left ventricular dysfunction. Angiography showed tight stenosis of the proximal left anterior descending (LAD) and borderline lesion in left circumflex coronary artery (LCX). She underwent percutaneous coronary intervention (PCI) to LAD with good result. Her blood tests showed low vitamin B12, folate and serum iron levels and elevated serum homocysteine level. She was given folic acid and vitamin B12 and her homocysteine levels normalized. This case demonstrates that hyperhomocysteinemia caused by nutritional deficiency of vitamin co factors may lead to MI. Hyperhomocysteinemia should be considered in the evalauation of young people with MI, especially those without conventional risk factors.
Sources of Power of Lower Participants in Complex Organizations

This paper explores various factors that account for the power of secretaries, hospital attendants, prison inmates, and other lower participants within organizations. Power is seen as resulting from access to and control over persons, information, and instrumentalities. Among the variables discussed affecting power are normative definitions, perception of legitimacy, exchange, and coalitions. Personal attributes related to power include commitment, effort, interest, willingness to use power, skills, and attractiveneses. Finally, various attributes of social structure are discussed which also help to account for the power of lower participants: time spent in the organization, centrality of position, duality of power structures, and replaceability of persons. ' David Mechanic is assistant professor of sociology, Department of Sociology, University of Wisconsin.
Concurrent Quantum Programming in Haskell

This paper applies established techniques for concurrent programming in Haskell to the case of Concurrent Quantum Programming. The foundation of the approach is an extension to Concurrent Quantum Programming of the technique of “virtual values” proposed by Amr Sabry for quantum programming in Haskell. The basic idea is to encapsulate quantum values within MVars, the monadic variables that support thread synchronization and mutually exclusive accesses to shared references. In this way, quantum processes can be concurring to have access to quantum values and we will be applying the now established quantum programming paradigm of “control is classic, data is quantum” to the concurrent and distributed quantum programming domain: the case in focus is that the control of concurrency is classical control, while shared data between quantum processes are quantum data. We illustrate the use of the proposed approach by programming sample algorithms for quantum teleportation and quantum cryptographic key distribution.
Combining Gradient Boosting Machines with Collective Inference to Predict Continuous Values

Gradient boosting of regression trees is a competitive procedure for learning predictive models of continuous data that fits the data with an additive non-parametric model. The classic version of gradient boosting assumes that the data is independent and identically distributed. However, relational data with interdependent, linked instances is now common and the dependencies in such data can be exploited to improve predictive performance. Collective inference is one approach to exploit relational correlation patterns and significantly reduce classification error. However, much of the work on collective learning and inference has focused on discrete prediction tasks rather than continuous. %target values has not got that attention in terms of collective inference. In this work, we investigate how to combine these two paradigms together to improve regression in relational domains. Specifically, we propose a boosting algorithm for learning a collective inference model that predicts a continuous target variable. In the algorithm, we learn a basic relational model, collectively infer the target values, and then iteratively learn relational models to predict the residuals. We evaluate our proposed algorithm on a real network dataset and show that it outperforms alternative boosting methods. However, our investigation also revealed that the relational features interact together to produce better predictions.
2 APPLICATIONS OF PERFORMANCE SCORING TO ACCOUNTS RECEIVABLE MANAGEMENT IN CONSUMER CREDIT

Performance scoring offers credit grantors improved ability to manage their consumer loan portfolios. The analytical models on which performance scoring are based employ data from creditors' accounts receivable ledgers reflecting customer payments and purchases or data from credit reporting agencies. Collected at the "Observation Point," these data reflect the behavior of customers during the "Performance Period," usually the preceding six or 12 months. The performance scoring system based on this information is designed to predict customer payment performance in the future, the "Outcome Period. " Creditors have frequently determined historical "roll rates," that is, the percentages of customers moving from one stage of delinquency to another. Thus, a creditor may know that 25 percent of his customers who are one payment delinquent at the "Observation Point" will be one payment or more delinquent within the next month. However, knowledge of the roll rate permits a creditor to forecast movement of customers through various categories of delinquency only on an average basis. Rather than focus attention on all customers that are one payment behind, it would be more efficient for a credit grantor to direct collection efforts only to that group of customers most likely to remain delinquent or likely to become more seriously behind in their payments. An illustration of how performance scoring permits creditors to focus on customers whose delinquency position is likely to worsen is provided using data developed from analysis of accounts in a bank credit card portfolio. Examples are based on past account behavior as well as on data from credit reports. A total of 10,000 accounts were one-month delinquent. However, under the performance scoring system based on account histories, only 2,000 accounts had a performance score of less than 50. Within this group of customers were included 55.7 percent of the customers that are expected to be one or two months delinquent during the next six months and 61.7 percent of the customers that will be three or more months delinquent during that period. Thus, by concentrating collection efforts on these accounts, a credit grantor will avoid antagonizing many customers likely to return to a current status and increase the likelihood of collecting from those whose delinquency is likely to worsen. Performance scoring may be used for a wide variety of credit decisions-targeting collection efforts; making credit line changes and purchase authorizations, and cross-selling other financial services. As creditors both reduce the cost and improve the …
Multiclass text categorization for automated survey coding

Survey coding is the task of assigning a symbolic code from a predefined set of such codes to the answer given in response to an open-ended question in a questionnaire (aka survey). We formulate the problem of automated survey coding as a text categorization problem, i.e. as the problem of learning, by means of supervised machine learning techniques, a model of the association between answers and codes from a training set of pre-coded answers, and applying the resulting model to the classification of new answers. In this paper we experiment with two different learning techniques, one based on naïmillve Bayesian classification and the other one based on multiclass support vector machines, and test the resulting framework on a corpus of social surveys. The results we have obtained significantly outperform the results achieved by previous automated survey coding approaches.
Cognitive behaviour therapy for first episode psychosis: Good for nothing or fit for purpose?

The main objective of this paper is to review the literature regarding studies of cognitive behaviour therapy (CBT) for people with first episode psychosis or early psychosis. A comprehensive search of the PsycINFO and MEDLINE databases identified twelve studies, including several randomised controlled trials and quasi‐experimental studies. There were few significant differences between groups at end of treatment or at follow‐up, and no differences in rates of relapse or readmission. The findings suggest, however, that CBT does have important benefits in terms of rate of recovery, improvements in certain symptoms (e.g. auditory hallucinations and hopelessness) and quality of life. Thus, there is modest support for the application of CBT for people experiencing early psychosis; however, the studies to date have difficulties with study design, theoretical underpinnings and how well the treatment targets fit with the CBT model. Future research addressing these issues is required to determine whether CBT for early psychosis may prove to be more effective than current data would suggest.
Dream Imagery as a Result of Emotions Matching with Images

In this article I argue that the function of matching is to bind together emotions and mental images; in so doing it plays an essential role in dream imagery formation. I take it further that matching is a necessary condition in the agency of mental awareness and that it operates in a similar way during both the waking state and dreaming.
Extreme Observations in the MENA Stock Markets and Their Implication for VAR Measures

The standard “delta-normal” Value at Risk methodology requires that the underlying returns generating distribution for the security in question is normally distributed, with moments which can be estimated using historical data and are time-invariant. However, the stylized fact that returns are fat-tailed is likely to lead to under-prediction of both the size of extreme market movements and the frequency with which they occur. In this paper, we use the extreme value theory to analyze four emerging . nancial markets belonging to the MENA region (Egypt, Jordan, Morocco, and Turkey). We focus on the tails of the unconditional distribution of returns in each market and provide estimates of their tail index behavior. In the process, we . nd that the returns have signi.cantly fatter tails than the normal distribution and therefore introduce the extreme value theory. We then estimate the maximum daily loss by computing the Value-at-Risk (VaR) in each market and explore the implications for portfolio diversi.cation and risk management. Generally, we . nd that the VaR estimates based on the tail-index are higher than those based on a normal distribution for all markets, and therefore a proper risk assessment should not neglect the tail behavior in these markets, since that may lead to an improper evaluation of market risk. Our results should be useful to investors, bankers, and fund managers, whose success depends on the ability to forecast stock price movements in these markets and therefore build their portfolios based on these forecasts.
Inference-less Density Estimation using Copula Bayesian Networks

We consider learning continuous probabilistic graphical models in the face of missing data. For non-Gaussian models, learning the parameters and structure of such models depends on our ability to perform efficient inference, and can be prohibitive even for relatively modest domains. Recently, we introduced the Copula Bayesian Network (CBN) density model - a flexible framework that captures complex high-dimensional dependency structures while offering direct control over the univariate marginals, leading to improved generalization. In this work we show that the CBN model also offers significant computational advantages when training data is partially observed. Concretely, we leverage on the specialized form of the model to derive a computationally amenable learning objective that is a lower bound on the log-likelihood function. Importantly, our energy-like bound circumvents the need for costly inference of an auxiliary distribution, thus facilitating practical learning of highdimensional densities. We demonstrate the effectiveness of our approach for learning the structure and parameters of a CBN model for two reallife continuous domains.
eGaN® FETs in High Frequency Resonant Converters

Distributed power systems are prevalent in telecommunications, networking, and high-end server applications and generally utilize a 48 V bus voltage adopted from the telecom industry. From the 48 V bus, a number of isolated point of load (POL) converters power the end loads, with isolation being required for safety. The traditional distributed power architecture (DPA), shown in figure 1(a), uses AC/DC front end converters to deliver the 48 V bus voltage. From the 48 V bus voltage, a number of regulated isolated DC/DC POL converters are used to deliver the required voltage and power to the individual loads. As communications, networking, and high-end server systems have become more complex, the voltages and currents demanded by the growing number of loads have increased significantly [1]. Having a large number of regulated 48 V isolated DC/DC POL converters to power these systems significantly increases the cost, volume, and complexity of the system.
Research on the Optimized Support Vector Regression Machines Based on the Differential Evolution Algorithm

The Support Vector Regression machine (SVR) is an effective tool to solve the problem of nonlinear prediction, but its prediction accuracy and generalization performances depend on the selection of parameters greatly. And the parameters selection is a procedure of global optimization search. Since the Differential Evolution (DE) population-based algorithm is a real- coding optimal algorithm with powerful global searching capacity, a hybrid model of DE-SVR based on the standard SVR model and DE algorithm is proposed in this paper. And then, the new hybrid implementation was applied to the short range regression prediction of the chaotic time series. At last, the experiment results showed the effectiveness of this approach and the better performance in searching time, compared with the conventional parameters searching approach of grid algorithm. algorithm has been achieved fine application effects in neural network training, filter design, cluster analysis. And this paper proposed a hybrid model based on the DE algorithm and the Support Vector Regression machine (SVR) model, which is called DE-SVR here. The proposed model is then applied to the short range regression forecast of the chaotic time series of Chens. The results of the proposed DE-SVR approach are compared with the results obtained by the conventional grid searching method and the PSO algorithm. Results presented show that the proposed DE-SVR approach appears to in higher prediction accuracy and faster searching.
Streptococcus pyogenes Pharyngeal Isolates with Reduced Susceptibility to Ciprofloxacin in Spain: Mechanisms of Resistance and Clonal Diversity

ABSTRACT A survey of emm gene sequences and an analysis of the pulsed-field electrophoretic profiles of 30 Streptococcus pyogenes isolates with reduced susceptibilities to ciprofloxacin detected the prevalence of isolates with emm type 6 and considerable genetic diversity among isolates. The mechanism of ciprofloxacin resistance in these isolates was based on point mutations in topoisomerase IV subunit C encoded by parC, mainly replacement of serine-79 by alanine.
Accommodating and Resisting Minority Language Policy: The Case of Wales

Any minority language policy must overcome two key difficulties if it is to be successfully implemented at state level. One is institutional - how can a minority language be legitimated and institutionalised in the public or civic realm when this has always been regarded previously as the preserve of the majority language? The other is attitudinal - how can a minority language gain sufficient support from majority language speakers for it to be accepted (and spoken regularly) as a state language? In this paper, I will explore these two difficulties, and the potential for their resolution, by discussing recent institutional developments in Welsh language policy in Wales - notably, the (1993) Welsh Language Act and the (1988) Education Reform Act - and by examining the attitudes and responses to such developments of a cohort of Welsh teacher trainees. The empirical data, drawing on both quantitative and qualitative analyses, will show that while minority language policies can garner considerable support at a general level, as is the case now in Wales, more specific aspects of bilingual policy remain openly contested. The latter is particularly so when aspects of minority language policy, such as bilingual requirements for public sector employment, appear to impinge on individual citizenship rights.
SERT: Self-Explanation Reading Training

This study examined the effects of providing reading strategy instruction to improve the effectiveness of self-explanation (i.e., explaining the meaning of information to oneself while reading). The effects of the reading strategy instruction, called Self-Explanation Reading Training (SERT), were examined both in terms of comprehension scores and self-explanation quality. Half of the participants (n = 42) received SERT, which included reading strategy instruction and self-explanation practice with 4 science texts (SERT condition). The remaining participants read aloud the 4 science texts (control condition). During this training phase, self-explanation, as compared to reading aloud, only improved comprehension for the most difficult of the 4 texts. Prior domain knowledge consistently improved comprehension performance, whereas reading skill and reading span had minimal effects. After training, both SERT and control participants self-explained a difficult text about cell mitosis. SERT improved comprehension and self-explanation quality only for participants with low domain knowledge. However, the effects of SERT on low-knowledge participants' comprehension emerged only for text-based questions and not for bridging-inference questions. Protocol analyses indicated that SERT helped these participants to use logic, or domain-general knowledge, rather than domain-specific knowledge to make sense of the text.
Enhanced leakage reduction technique by gate replacement

Input vector control (IVC) technique utilizes the stack effect in CMOS circuit to apply the minimum leakage vector (MLV) to the circuit at the sleep mode to reduce leakage. Additional logic gates can be inserted as control points to make it more effective. In this paper, we propose a gate replacement technique that further enhances the leakage reduction. The basic idea is to replace a gate that is in its worst leakage state by another library gate while keeping the circuit's correct functionality at the active mode. We also develop a divide-and-conquer approach that integrates a fast gate replacement heuristic, an optimal MLV search strategy for tree circuit, and a genetic algorithm to connect the tree circuits. We conduct experiments on the MCNC91 benchmark circuits. The results reveal that our technique can reduce additional 10% to 24% leakage over the best known IVC methods and the optimal MLV with no delay penalty and little area increase.