Miracle-dz/UniFAR-FTUs huggingface dataset:
Double Standards: The Role of Gender and Intraparty Politics in Swedish Cabinet Appointments

Are female politicians less likely to be promoted to specific ministerial posts, and is it important for them to toe the party line? This article focuses on whether the selection of ministers has a gender-specific dimension. Building on role congruity theory and research showing that female and male politicians are evaluated in a different manner in leadership positions, we present some original hypotheses. For example, we hypothesize that female politicians are less likely to be appointed to cabinet when they have held gender-incongruent committee positions in parliament. We also hypothesize that women are less likely to be appointed to cabinet posts when they have previously deviated from the party line. In an empirical analysis of Swedish ministerial appointments in six cabinets, we find that female politicians were less likely to be appointed to cabinet posts when they have held positions in ‘masculine’ parliamentary committees and when they deviated from the party line in their parliamentary speeches. These results suggest that women are more harshly judged when holding positions that are not in line with traditional gender stereotypes and have important implications for our understanding of gender and political leadership.
MPPT and control design of a Vienna rectifier-based low power wind turbine with reduced number of sensors

Low power wind turbines (LPWT) are an interesting option for distributed generation. Maximum power point tracking (MPPT) procedures for LPWT are either based on look-up tables or use an algorithm. This paper proposes an MPPT algorithm for wind energy analogous to that of the incremental inductance for solar energy. The Vienna rectifier is an efficient non-regenerative power converter with only three switching devices and its use for LPWT can be advantageous. The permanent magnet synchronous generator (PMSG) is a common option for LPWT because of its efficiency. This manuscript explains the sensorless vector control of PMSG using the Vienna rectifier, which results to be analogous to that of the traditional full bridge converter for unity power factor. In addition, the unbalanced voltage in the Vienna rectifier capacitors is derived from the modulation indexes without requiring an additional sensor. Hence, the resulting LPWT has low losses by virtue of the Vienna rectifier, and lower cost and higher reliability by virtue of the sensorless vector control and unbalance voltage estimation. Simulation results validate the proposals of the paper.
MLSP Data Analysis Competition 2006: Denoising of Magnetoencephelographic Data

The study of human cognition, and preoperative functional brain mapping, are facilitated through the use of magnetoencephalography (MEG). However, the noise present in such recordings is significant relative to the signals of interest. To circumvent this issue multiple trials are performed for the same task and an ensemble average is performed to increase the signal-to-interference/noise ratio (SNIR). Unfortunately, perhaps 100-500 trials are required to achieve a sufficiently large SNIR. This paper describes a simple technique for improving the SNIR value given only 10 trials. The 10 trials from each of the 274 channels are first averaged. The 274 averaged channel estimates are then temporally low passed filtered, adjusting for the phase shift introduced by the filtering process. Finally, a spatial average is performed, where each channel is assigned the average value of itself and the three most similar channels. The three most similar channels are identified using correlation. The described method achieves an SNIR of 0.6 dB.
Playpen: Toward an Architecture for Modeling the Development of Spatial Cognition

In this report we argue that the study of the acquisition of word meaning requires taking seriously non-linguistic cognition, in particular human vision and the pre-linguistic development of concepts. We consider the implications of this claim for the acquisition of spatial relations, and we present Playpen, an evolving neural network architecture for modeling the development of spatial language and spatial cognition. Playpen includes modules for high-level vision, the lexicon, and the conceptual space in which vision and lexicon come together, allowing for the mutual in uence of all three. This report focuses on the basic building blocks of the network. Feature binding and object segregation are implemented through the use of phase angles, and the learning algorithm is a version of Contrastive Hebbian Learning (Movellan, 1990), adapted for units with phase angles. We argue that to represent and learn the meanings of relational terms, the network also requires units which represent micro-relations explicitly. In Playpen these take the form of relation units, hard-wired clusters of simpler units which become activated to the extent that they receive inputs from units representing distinct objects.
Connectionist Optimisation of Tied Mixture Hidden Markov Models

Issues relating to the estimation of hidden Markov model (HMM) local probabilities are discussed. In particular we note the isomorphism of radial basis functions (RBF) networks to tied mixture density modelling; additionally we highlight the differences between these methods arising from the different training criteria employed. We present a method in which connectionist training can be modified to resolve these differences and discuss some preliminary experiments. Finally, we discuss some outstanding problems with discriminative training.
Lexical Scoring System of Lexical Chain for Quranic Document Retrieval

An Information Retrieval (IR) system aims to extract information based on a query made by a user on a particular subject from an extensive collection of text. IR is a process through which information is retrieved by submitting a query by a user in the form of keywords or to match words. In the Al-Quran, verses of the same or comparable topics are scattered throughout the text in different chapters, and it is therefore difficult for users to remember the many keywords of the verses. Therefore, in such situations, retrieving information using semantically related words is useful. In well-composed documents, the semantic integrity of the text (coherence) exists between the words. Lexical cohesion is the results of chains of related words that contribute to the continuity of the lexical meaning found within the text are a direct result of text being about the same thing (i.e. topic, etc.). This indicates that using an IR system and lexical chains are a useful and appropriate method for representing documents with concepts rather than using terms in order to have successful retrieval based on semantic relations. Therefore, a new Lexical Scoring System is proposed in this study, in addition to determining the semantic relation that exists between words whereby WordNet was used as the semantic knowledge base. The proposed scoring system helped to retrieve 86.58% of the total relevant documents in the Al-Quran based on the relevance judgment, using the lexical chain approach. Based on the findings, the study concludes that, the proposed approach on representing verses using lexical chains is appropriate and suitable for a Quranic IR system.
On the Robustness of Primal-Dual Iterative Combinatorial Auctions

Though the VCG auction assumes a central place in the mechanism design literature, there are a number of reasons for favouring iterative combinatorial auction designs. Several promising ascending auction formats have been developed throughout the past few years based on primal-dual algorithms and linear programming theory. Prices are interpreted as a feasible dual solution and the provisional allocation is interpreted as a feasible primal solution. iBundle(3) (Parkes and Ungar 2000), dVSV (de Vries et al. 2007) and the Ascending Proxy auction (Ausubel and Milgrom 2002) result in VCG payoffs when the coalitional value function satisfies buyer submodularity conditions and bidders bid straightforward, which is an ex-post Nash equilibrium. iBEA and the Credit-Debit auction (Mishra and Parkes 2007) do not even require the buyer submodularity condition and achieve the same properties for general valuations. In many situations, however, one can not assume bidders to bid straightforward and it is not clear from the theory how primal-dual auctions perform in these situations. Robustness of auctions wrt. different bidding behaviour is therefore a critical issue. We have conducted sensitivity analyses for these primal-dual auction designs with respect to different bidding strategies and different valuation models. In this paper, we compare the results of primal-dual auctions to those of the VCG auction and those of iterative combinatorial auction formats with approximate linear prices, such as ALPS (Bichler et al. 2007) and the Combinatorial Clock auction (Porter et al. 2003). While primal-dual auctions performed very well in case of straightforward bidding, we could observe problems with respect to auctioneer revenue, efficiency, and speed of convergence in case of heuristic bidding behaviour. Interestingly, linear price combinatorial auctions have shown to be fairly robust against these strategies.
Are Wealth Effects Important for Canada

The authors examine the link between consumption and disaggregate wealth in Canada. They use a vector-error-correction model in which permanent and transitory shocks are identified using the restrictions implied by cointegration proposed by King, Plosser, Stock, and Watson (1991) and Gonzalo and Granger (1995). This procedure allows the authors to identify the reaction of consumption to both types of shocks and to calculate average marginal propensities to consume out of disposable income, human wealth, stock market wealth, and housing wealth. The authors find evidence of a significant housing wealth effect for Canada. Conversely, the evidence regarding the stock market wealth effect is weak. In terms of policy implications, other things being equal, the analysis of future inflationary pressures would require that more weight be put on fluctuations in housing prices than on fluctuations in stock prices.
Weakly Supervised Tweet Stance Classification by Relational Bootstrapping

Supervised stance classiﬁcation, in such domains as Congressional debates and online forums, has been a topic of interest in the past decade. Approaches have evolved from text classiﬁcation to structured output prediction, including collective classiﬁcation and sequence labeling. In this work, we investigate collective classiﬁcation of stances on Twitter, using hinge-loss Markov random ﬁelds (HL-MRFs). Given the graph of all posts, users, and their relationships, we constrain the predicted post labels and latent user labels to correspond with the network structure. We focus on a weakly supervised setting, in which only a small set of hashtags or phrases is labeled. Using our relational approach, we are able to go beyond the stance-indicative patterns and harvest more stance-indicative tweets, which can also be used to train any linear text classi-ﬁer when the network structure is not available or is costly.
Eye-gaze estimation accuracy and key in human vision

Eye movement and eye-gaze direction have significant roles in human communications. Eye-gaze direction often indicates what a person is thinking about or trying to do. Empirically, it is possible to recognize which eye (right or left) is being fixated by a person who is standing in front of us at 1 m distance; this indicates that our eye-gaze estimation accuracy is less than 3.7 deg. This paper describes our specific examination of eye-gaze direction estimation accuracy and method in the human vision system. Extensive measurements show that the eye-gaze estimation accuracy is 2-4 deg from 1 m distance. However, they also indicate that individual eye-gaze estimation errors differ in magnitude and tendency. A person who has good eyesight estimates well, in general. Furthermore, measurements reveal that human vision might use an eyeball model-based method that employs the center of the iris or pupil, rather than an iris shape-based method with an elliptical iris shape.
Customers’ Responses to Employee Extra Attention

Focusing on positive service experiences, this study examined the relation-ships among employee extra attention, customers delight, and customer re-purchase intentions. The target sample that had a positive interaction with an employee in the recent six months was invited to participate in the survey. The data analysis was performed in two steps: 1) Confirmatory Factor Analysis (CFA) and 2) Structural Equation Modeling (SEM). The results suggested that employees’ extra attention affects customer delight. Subsequently, customer delight has a positive relationship with customer repurchase intentions. Furthermore, employee extra attention has a positive relationship with customer repurchase intentions. The finding of the study provides valuable implications for research and the industry regarding generating favorable customer repurchase intentions in positive service experiences.
MORPHOLOGY OF THE INTERSTELLAR COOLING LINES DETECTED BY COBE

The Far-Infrared Absolute Spectrophotometer (FIRAS) on the COBE satellite has conducted an unbiased survey of the far-infrared emission from our Galaxy. The first results of this survey were reported by Wright et al. (1991). We report the results of new analyses of this spectral survey, which includes emission lines from 158 micrometer C(+), 122 and 205 micrometer N(+), 370 and 609 micrometer C(0), and CO J = 2 goes to 1 through J = 5 goes to 4. We report the morphological distribution along the Galactic plane (b = 0 deg) of the spectral line emission, and the high Galactic latitude intensities of the C(+) and 205 micrometer N(+) emission. In the Galactic plane the 205 micrometer line of N(+) generally follows the 158 micrometer C(+) line distribution, but the intensities scale as I(N(+) 205 micrometer) varies as I(C(+) 158 micrometer)(exp 1.5) toward the inner Galaxy. The high Galactic latitude intensity of the 158 micrometer fine-structure transition from C(+) is I(C(+) 158 micrometer) = (1.43 +/- 0.12) x 10(exp -6) csc (absolute value of b) ergs/sq cm s sr for absolute value of b greater than 15 deg, and it decreases more rapidly than the far-infrared intensity with increasing Galactic latitude. C(+) and neutral atomic hydrogen emission are closely correlated with a C(+) cooling rate of (2.65 +/- 0.15) x 10(exp -26) ergs/s. We conclude that this emission arises almost entirely from the cold neutral medium. The high Galactic latitude intensity of the 205 micrometer fine-structure transition from N(+) is I(N(+) 205 micrometer) = (4 +/- 1) x 10(exp -8) csc (absolute value of b) ergs/((sq cm)(s)(sr)) arising entirely from the warm ionized medium. We estimate the total ionizing photon rate in the Galaxy to be phi = 3.5 x 10(exp 53) ionizing photons per second, based on the 205 micrometer N(+) transition.
Acoustic and articulatory analysis on Japanese vowels in emotional speech

Acoustic and Articulatory features of Japanese vowels were examined in “Neutral”, “Angry”, and “Sad” speech, using NDI Wave System. The results suggest that (1) Significant differences of the acoustic space, measured by F1 and F2, exist among different emotions. “Angry” is characterized by a horizontally compressed acoustic space, while “Sad” is characterized by a vertically compressed acoustic space. (2) The “front raising” and “retraction and back raising” patterns of the tongue movement mechanism can be enhanced by “Angry” and “Sad” emotion. (3) The lips' dynamically protruding feature is shared by both “Angry” and “Sad”, apart from the exception [A]. We suggested that the exception is caused by the increase of the mouth opening. The mouth opening and the degree of lip protrusion are a pair of complementary features. (4) In articulatory domain, “Angry” is characterized by an increase of mouth opening and a reducing of tongue horizontal movement range.
Toward dependency-aware live virtual machine migration

The most powerful characteristic of any machine virtualization technology is its ability to adapt to both its underlying infrastructure and the applications it supports. Possibly the most dynamic feature of machine virtualization is the ability to migrate live virtual machines between physical hosts in order to optimize performance or avoid catastrophic events. Unfortunately, the need for live migration increases during times when resources are most scarce. For example, load-balancing is only necessary when load is significantly unbalanced and impending downtime often causes many virtual machines to seek new hosts simultaneously. It is imperative that live migration mechanisms be as fast and efficient as possible in order for virtualization to provide dynamic load balancing, zero-downtime scheduled maintenance, and automatic failover during unscheduled downtime. This paper proposes a novel dependency-aware approach to live virtual machine migration and presents the results of the initial investigation into its ability to reduce migration latency and overhead. The approach uses a tainting mechanism originally developed as an intrusion detection mechanism. Dependency information is used to distinguish processes that create direct or indirect external dependencies during live migration. It is shown that the live migration process can be significantly streamlined by selectively applying a more efficient protocol when migrating processes that do not create external dependencies during migration.
Selective Sampling Using Random Field

Most existing inductive learning algorithms assume the availability of a training set of labeled examples. In many domains, however, labeling the examples is a costly process that requires either intensive computation or manual labor. In such cases, it may be beneecial for the learner to be active by intelligent selection of examples for labeling with the goal of reducing the labeling cost. In this paper we propose a lookahead algorithm for selective sampling of examples for nearest-neighbors classiiers. The algorithm attempts to nd the example with the highest utility considering its eeect on the resulting classiier. Computing the expected utility of an example requires estimating the probability of the possible labels. We propose to use the random eld model for this estimation. The proposed selective sampling algorithm was evaluated empirically on real and artiicial data sets. The experiments show that the proposed algorithm outperforms other methods.
Considering long‐memory when testing for changepoints in surface temperature: A classification approach based on the time‐varying spectrum

Changepoint models are increasingly used to represent changes in the rate of warming in surface temperature records. On the opposite hand, a large body of literature has suggested long‐memory processes to characterize long‐term behavior in surface temperatures. While these two model representations provide different insights into the underlying mechanisms, they share similar spectrum properties that create “ambiguity” and challenge distinguishing between the two classes of models. This study aims to compare the two representations to explain temporal changes and variability in surface temperatures. To address this question, we extend a recently developed time‐varying spectral procedure and assess its accuracy through a synthetic series mimicking observed global monthly surface temperatures. We vary the length of the synthetic series to determine the number of observations needed to be able to accurately distinguish between changepoints and long‐memory models. We apply the approach to two gridded surface temperature data sets. Our findings unveil regions in the oceans where long‐memory is prevalent. These results imply that the presence of long‐memory in monthly sea surface temperatures may impact the significance of trends, and special attention should be given to the choice of model representing memory (short versus long) when assessing long‐term changes.
Having Your Cake and Eating It Too: Jointly Optimal Erasure Codes for I/O, Storage, and Network-bandwidth

Erasure codes, such as Reed-Solomon (RS) codes, are increasingly being deployed as an alternative to data-replication for fault tolerance in distributed storage systems. While RS codes provide significant savings in storage space, they can impose a huge burden on the I/O and network resources when reconstructing failed or otherwise unavailable data. A recent class of erasure codes, called minimum-storage-regeneration (MSR) codes, has emerged as a superior alternative to the popular RS codes, in that it minimizes network transfers during reconstruction while also being optimal with respect to storage and reliability. However, existing practical MSR codes do not address the increasingly important problem of I/O overhead incurred during reconstructions, and are, in general, inferior to RS codes in this regard. In this paper, we design erasure codes that are simultaneously optimal in terms of I/O, storage, and network bandwidth. Our design builds on top of a class of powerful practical codes, called the product-matrix-MSR codes. Evaluations show that our proposed design results in a significant reduction the number of I/Os consumed during reconstructions (a 5× reduction for typical parameters), while retaining optimality with respect to storage, reliability, and network bandwidth.
Minimum Distance Estimation of Dynamic Models with Errors-in-Variables

Empirical analysis often involves using inexact measures of desired predictors. The bias created by the correlation between the problematic regressors and the error term motivates the need for instrumental variables estimation. This paper considers a class of estimators that can be used when external instruments may not be available or are weak. The idea is to exploit the relation between the parameters of the model and the least squares biases. In cases when this mapping is not analytically tractable, a special algorithm is designed to simulate the latent predictors without completely specifying the processes that induce the biases. The estimators perform well in simulations of the autoregressive distributed lag model and the dynamic panel model. The methodology is used to re-examine the Phillips curve, in which the real activity gap is latent.
Towards Convenient Calibration for Cross-Ratio Based Gaze Estimation

Eye gaze movements are considered as a salient modality for human computer interaction applications. Recently, cross-ratio (CR) based eye tracking methods have attracted increasing interest because they provide remote gaze estimation using a single uncalibrated camera. However, due to the simplification assumptions in CR-based methods, their performance is lower than the model-based approaches [8]. Several efforts have been made to improve the accuracy by compensating for the assumptions with subject specific calibration. This paper presents a CR-based automatic gaze estimation system that accurately works under natural head movements. A subject-specific calibration method based on regularized least-squares regression (LSR) is introduced for achieving higher accuracy compared to other state-of-the-art calibration methods. Experimental results also show that the proposed calibration method generalizes better when fewer calibration points are used. This enables user friendly applications with minimum calibration effort without sacrificing too much accuracy. In addition, we adaptively fuse the estimation of the point of regard (PoR) from both eyes based on the visibility of eye features. The adaptive fusion scheme reduces accuracy error by around 20% and also increases the estimation coverage under natural head movements.