Gender gap in parental leave intentions: Evidence from 37 countries

This is the final version. Available from Wiley via the DOI in this record. Despite global commitments and efforts, a gender-based division of paid and unpaid work persists. To identify how psychological factors, national policies, and the broader sociocultural context contribute to this inequality, we assessed parental-leave intentions in young adults (18–30years old) planning to have children (N = 13,942; 8,880 identified as women; 5,062 identified as men) across 37 countries that varied in parental-leave policies and societal gender equality. In all countries, women intended to take longer leave than men. National parental-leave policies and women’s political representation partially explained cross-national variations in the gender gap. Gender gaps in leave intentions were paradoxically larger in countries with more gender-egalitarian parental-leave policies (i.e., longer leave available to both fathers and mothers). Interestingly, this cross-national variation in the gender gap was driven by cross-national variations in women’s (rather than men’s) leave intentions. Financially generous leave and gender-egalitarian policies (linked to men’s higher uptake in prior research) were not associated with leave intentions in men. Rather, men’s leave intentions were related to their individual gender attitudes. Leave intentions were inversely related to career ambitions. The potential for existing policies to foster gender equality in paid and unpaid work is discussed.SSHRC Insight Development GrantSSHRC Insight GrantEconomic and Social Research CouncilState Research AgencyGuangdong 13th-five Philosophy and Social Science Planning ProjectNational Natural Science Foundation of ChinaSwiss National Science FoundationSwiss National Science FoundationCenter for Social Conflict and Cohesion StudiesCenter for Intercultural and Indigenous ResearchSSHRC Postdoctoral FellowshipSlovak Research and Development AgencySwiss National Science FoundationCanada Research ChairsSocial Sciences and Humanities Research Council of CanadaOntario Ministry of Research and InnovationHSE University, RFFaculty of Arts, Masaryk Universit

Adaptive Thresholding for Sparse Image Reconstruction

The performance of the class of sparse reconstruction algorithms which is based on the iterative thresholding is highly dependent on a selection of the appropriate threshold value, controlling a trade-off between the algorithm execution time and the solution accuracy. This is why most of the state-of-the-art reconstruction algorithms employ some method of decreasing the threshold value as the solution converges toward the optimal one. To address this problem we propose a data-driven adaptive threshold selection method based on the fast intersection of confidence intervals (FICI) method, with which we have augmented the two-step iterative shrinkage thresholding (TwIST) algorithm. The performance of the proposed algorithm, denoted as the FICI-TwIST algorithm, has been evaluated on a problem of image reconstruction with the missing pixels, exploiting image sparsity in the discrete cosine transformation domain. The obtained results have shown competitive performance in comparison with a number of state-of-the-art sparse reconstruction algorithms, even outperforming them in some scenarios

Signal content estimation based on the short-term time-frequency Rényi entropy of the S-method time-frequency distribution

A key characteristic of a nonstationary signal, when analyzed in the time-frequency domain, is the signal complexity, quantified as the number of components in the signal. This paper describes a method for the estimation of this number of components of a signal using the short-term Rényi entropy of its time-frequency distribution (TFD). We focus on the characteristics of TFDs that make them suitable for such a task. The performance of the proposed algorithm is studied with respect to the parameters of the S-method TFD, which combines the virtues of both the spectrogram and the Wigner-Ville distribution. Once the optimal parameters of the TFD have been determined, the applicability of the method in the analysis of signals in low SNRs and real life signals is assessedScopu

Signal content estimation based on the short-term time-frequency Rényi entropy of the S-method time-frequency distribution

A key characteristic of a nonstationary signal, when analyzed in the time-frequency domain, is the signal complexity, quantified as the number of components in the signal. This paper describes a method for the estimation of this number of components of a signal using the short-term Rényi entropy of its time-frequency distribution (TFD). We focus on the characteristics of TFDs that make them suitable for such a task. The performance of the proposed algorithm is studied with respect to the parameters of the S-method TFD, which combines the virtues of both the spectrogram and the Wigner-Ville distribution. Once the optimal parameters of the TFD have been determined, the applicability of the method in the analysis of signals in low SNRs and real life signals is assessedScopu

Measures, performance assessment, and enhancement of TFDs

This chapter describes a number of time-frequency (t,f) performance quality measures specifically developed as criteria for performance enhancement for a given application. The adopted performance measures are defined using objective criteria followed by time-frequency distribution (TFD) enhancement methods to improve the (t,f) concentration, resolution, and readability of TFDs. The topic is covered in nine articles. Hyperbolic FM signals are well described by a method related to time-scale analysis and the wavelet transform (Section 7.1). A general procedure for enhancing the time-frequency resolution and readability of TFDs is the reassignment principle described in Section 7.2. Techniques for measuring the concentration of TFDs and for automatic optimization of their parameters are presented based on entropy measures (Section 7.3). Another approach defines a resolution performance measure using local measurements in the (t,f) domain, such as relative amplitudes of auto-terms and cross-terms (Section 7.4). Then, attempts to unify time-frequency, time-scale, filter banks, wavelets, and the discrete-time Gabor transform using product functions and cascaded frames are presented briefly as they may assist in the selection of the best-performing method for a given application (Section 7.5). The last four topics focus on (1) time-frequency compressive sensing (Section 7.6); (2) signal complexity estimation using (t,f) entropy measures (Section 7.7); (3) time-frequency analysis using neural networks (Section 7.8); and (4) a comparison of postprocessing methods in the (t,f) domain (Section 7.9).Scopu

Time frequency and array processing of non-stationary signals

The special issue of EURASIP Journal on Advances in Signal Processing 2012 focuses on the synergistic relationship between time-frequency methods and array signal processing methods and addresses recent developments. In the article 'Joint DOD/DOA estimation in MIMO radar exploiting time-frequency signal representations' Yimin Zhang and co-researchers deal with the joint estimation of direction-of-departure (DOD) and direction-of-arrival (DOA) information of maneuvering targets in a bistatic multiple-input multiple-output (MIMO) radar system when exploiting spatial time-frequency distribution (STFD). In the article 'Estimating the number of components of a multicomponent nonstationary signal using the short term time-frequency Rényi entropy,' Victor Sucic and co-researchers propose a solution to the problem of detecting the local number of signal components by resorting to the short-term Rényi entropy of signals in the time-frequency plane

Instantaneous frequency estimation and localization

In many applications, a critical feature of a non-stationary signal is provided by its instantaneous frequency (IF), which accounts for the signal spectral variations as a function of time. This chapter presents methods and algorithms for the localization and estimation of the signal IF using time-frequency (t,f) based methods. The topic is covered in seven sections with appropriate internal cross-referencing to this and other chapters. In addition to filter banks and zero-crossings, one of the first conventional approaches for IF estimation used the spectrogram. To account for its major limitations related to accuracy, resolution, window dependence, and sensitivity, improvements were made by introducing iterative methodologies on the estimate provided by the first moment of the spectrogram (Section 10.1). Another approach uses an adaptive algorithm for IF estimation using the peak of suitable TFDs with adaptive window length (Section 10.2). This method was extended to the case of multicomponent signals using high-resolution TFDs such as the modified B-distribution (Section 10.3). When the signals considered have polynomial FM characteristics, both the peak of the polynomial WVD and higher-order ambiguity functions can be used as IF estimators (Section 10.4). In the special case when the signals are subject to random amplitude modulation (or multiplicative noise), IF estimation procedures are described using the peak of the WVD for linear FM signals, and the peak of the PWVD for nonlinear FM signals (Section 10.5). Then, a comparison of multicomponent IF estimation algorithms is provided (Section 10.6); and methods for IF and polynomial phase parameters estimation using linear (t,f) representations are presented (Section 10.7). Next, linear (t,f) methods are described for IF and polynomial phase parameter estimation. Finally, the concept of particle filtering is used for sequential Bayesian estimation of the IF (Section 10.8).Scopu

Instantaneous frequency estimation and localization

In many applications, a critical feature of a nonstationary signal is provided by its instantaneous frequency (IF), which accounts for the signal spectral variations as a function of time. This chapter presents methods and algorithms for the localization and estimation of the signal IF using time-frequency (t,f) based methods. The topic is covered in eight sections with appropriate internal cross-referencing to this and other chapter