Narrow Results

This paper explores the issue of discrimination against Asian migrants relative to their non-Asian counterparts in the Australian labour market. A unique and consistent data set from three waves of the Longitudinal Survey of Immigrants to Australia (LSIA, 1993–95) is used to estimate probit models of the probability of being unemployed separately for males and females of Asian and non-Asian origins. The unemployment probability gap between the two migrant groups is decomposed into two components, the first associated with differences in their human capital and other demographic characteristics, and the second with differences in their impacts (called discrimination). The results provide an evidence of discrimination against Asian male migrants in all three waves. Discrimination against Asian females is detected only in the first wave. The Asian females who are professionals and can speak English 'well' are rather favoured relative to their non-Asian counterparts. Thus, the empirical evidence on discrimination against migrants of Asian origin is mixed.

Semiparametric estimation has gained significant attention in the study of wage inequality between men and women in recent years. By extending the wage gap at the mean towards the entire wage distribution using quantile regression, it enables researchers to ascertain the direction and the proportions of differences in characteristics and returns to these characteristics at different parts of the wage distribution. This line of research has been prominent in western society but has not yet been explored in the context of the Malaysian labor market. To fill the gap, this paper examines the gender earnings gap in Malaysia between 1994 and 2004 using Malaysia Population and Family Survey data. The gender earnings differential, as measured by the log percentage point is 53% in 1994. The difference reduces to 45% for a restricted sample and 42% for the unrestricted sample in 2004. However, it was found that the gender wage gap reduces as we move up the wage distribution. This suggests that women suffer from a sticky floor effect, i.e., the gender wage gap is bigger at the bottom of distribution. More importantly, the observed gender wage differentials do not reflect differences in the productive characteristics of the workers. In fact, it accounts for very little, if any, of the gap in Malaysia. However, the extent of the price effect is larger at the bottom end of the distribution than at the top.

This paper estimates the gender wage gap and its composition in China’s urban labor market. The traditional Blinder–Oaxaca (1973) decomposition method with different weighing systems is employed. To correct for potential selection bias caused by women’s labor force participation, we employ the Heckman’s two-step procedure to estimate the female wage function. A large proportion of the gender wage gap is unexplained by differences of productive characteristics of individuals. Even though women have higher level of education attainments on average, they receive lower wages than men. Both facts suggest a potential discrimination against women in China.

The study examines the extent of gender- and caste-based discrimination among the formally and informally employed in India using the National Sample Survey Office (NSSO) Employment-Unemployment Survey (EUS) data for the four major rounds from 1999–00 to 2011–12. Oaxaca-Blinder decomposition results corrected for self-selection show wage discrimination to be significantly higher in informal employment compared to the formally employed. Similarly, caste-based discrimination is found to be lower compared to gender-based discrimination. The quantile decomposition results show discrimination to vary across the quantiles. Our results highlight the need for better regulation of the informal labor market in India.

We review recent research on methods for selecting features for multidimensional pattern classification. These methods include nonmonotonicity-tolerant branch-and-bound search and beam search. We describe the potential benefits of Monte Carlo approaches such as simulated annealing and genetic algorithms. We compare these methods to facilitate the planning of future research on feature selection.

Recently, much interest has been generated regarding speech recognition systems based on Hidden Markov Models (HMMs) and neural network (NN) hybrids. Such systems attempt to combine the best features of both models: the temporal structure of HMMs and the discriminative power of neural networks. In this work we establish one more relation between the HMM and the NN paradigms by introducing the time-warping network (TWN) that is a generalization of both an HMM-based recognizer and a backpropagation net. The basic element of such a network, a time- warping neuron, extends the operation of the formal neuron of a backpropagation network by warping the input pattern to match it optimally to its weights. We show that a single-layer network of TW neurons is equivalent to a Gaussian density HMM-based recognition system. This equivalent neural representation suggests ways to improve the discriminative power of this system by using backpropagation discriminative training, and/or by generalizing the structure of the recognizer to a multi-layer net. The performance of the proposed network was evaluated on a highly confusable, isolated word, multi-speaker recognition task. The results indicate that not only does the recognition performance improve, but the separation between classes is enhanced, allowing us to set up a rejection criterion to improve the confidence of the system.

The application of an elastic graph-matching approach to discriminate facial image regions is presented. In contrast to the dynamic link architecture introduced by the Malsburg group, our application is not an identification task but a classification task. Therefore, our approach differs in several important aspects:

(1) the choice of the filter set,

(2) the selection of the positions of the nodes of the graph to represent the characteristic image information,

(3) the generation of a representative reference pattern needed for the calculation of the classifications, and

(4) a new two-step graph-matching approach based on the simulated annealing technique.

The approach was tested on facial regions taking the eye region as an example target. A classification performance for the verification of eye regions of more than 93% was achieved.

Based on the continuous Wavelet Transform Modulus Maxima method (WTMM), a multifractal analysis was introduced to discriminate the irregular fracture signals of materials. This method provides an efficient numerical technique to characterize statistically the local regularity of fractures.

The results obtained by this nonlinear analysis suggest that multifractal parameters such as the capacity dimension D₀, the average singularity strength α₀, the aperture of the left side (α₀ - α_min) and the total width (α_max - α_min) of the D(α) spectra allow a better fit for the characterization of the different fracture stages.

Discriminating the three principal stages of the fracture namely the fracture initiation, the fracture propagation and the final rupture, provides a powerful diagnostic tool to identify the crack initiation site, and thus delineates the causes of the cracking of the material.

Ionizing particles detection based on phonons counting are considered as a growing research point of great interest. Phononic crystal (PnC) detectors have a higher resolution than other detectors. In the present work, we shall prepare a setup of a radiation detector based on a 1D PnC. The PnC detector can be used in detection and discrimination between protons and alpha particles with incident energy 1MeV. We have proposed a model capable of filtering the energies of two different ionizing particles (proton and alpha particle) of specific lattice frequencies in steps. Firstly, the high probability of phonons production was found at transmitted energy 5KeV from the whole path of protons and alpha particles through a vertical thin sheet made from Mylar and Polymethyl methacrylate (PMMA), respectively. The outgoing elastic waves are subjected to propagate through the proposed PnCs structure (Teflon-Polyethylene)² that shows the different transmission percentage to each particle. Therefore, the detection and discrimination between ionizing ions were achieved.

Asia-Pacific: Falling behind in the fight against HIV/AIDS

In order to highlight the cardiac sounds or phonocardiogram (PCG) signals analysis according to their added murmur importance, we try to apply the wavelet transform in its multi resolution analysis version. We then look for reconstruction error between the original signal and the synthesized signal. In this case, the original PCG signal is decomposed over seven levels and the seventh detail of decomposition is considered as the synthesized signal. According to the results we obtain, the reconstruction error can be considered as an important parameter in the classification and discrimination of the pathological severity of the PCG signals.

The phonocardiogram (PCG) signal is sometimes affected by added parameters that reflect the presence of a specific pathology. The intensity or the energy of the signal is one of the most reliable parameters when studying cardiac severity. Yet, in a pathological electrophysiological and audio signal, the severity information does not fully remain in the intensity or energy, but in other variables. In this paper, we will discuss the ability of a time-frequency parameter to discriminate, separate, and monitor the pathological cardiac severity levels. We studied 14 PCG signal from eight pathologies, six of them contain clicks (reduce murmurs), and eight murmur PCG signals with four different cardiac severity levels. We then calculated the entropy of approximation coefficients (EAC) from a discrete wavelet transform (DWT) analysis, to differentiate the PCG signals with clicks from those with murmurs and to assess the cardiac severity evolution. Since the entropy EAC is also related to the signal’s intensity (energy), we compared it to the energetic ratio (ER) evolution, a parameter widely used for PCG signals discrimination and classification, which revealed that the EAC provied better results for the paper' purposes.

Deep face recognition model learned on big dataset surpasses humans on difficult unconstrained face dataset. But open set face recognition, i.e. robust to both variations and unknown faces, is still a big challenge. In this paper, we propose a robust open set face recognition approach with deep transfer learning and extreme value statistics. First, we demonstrate that transferring the feature representations of a pre-trained deep face model to specific tasks is an efficient and effective approach for face recognition on small datasets. We learn both higher layer representations and the final linear multi-class SVMs with transferred features. Second, we propose a novel approach for unknown people recognition with extreme value statistics. Different from traditional distribution fitting, our approach only makes use of a simple statistical quantity — standard deviation of tail data. Empirical evidence shows that standard deviation of the tail of multi-class SVMs recognition scores is efficient and robust for unknown people recognition. Finally, we also empirically explore an important open problem — attributes and transferability of different layer features of the deep model. We argue that lower layer features are both local and general, while higher layer ones are both global and specific which embrace both intra-class invariance and inter-class discrimination. The results of unsupervised feature visualization and supervised face identification strongly support our view.

Within the UK, construction has an unenviable status as being the industry with the lowest representation of women and ethnic minority employees. Despite considerable efforts to diversify the industry's labor force, this has had little tangible effect on the numbers of these non-traditional entrants. Empirical studies that have explored aspects of women's and ethnic minorities' employment have tended to deal with the experiences of these under-represented groups separately. In contrast, this paper uses the findings from two research studies to compare women and ethnic minority employees' experiences of gaining employment and working within the industry. Both studies suggest the construction workplace presents a challenging and hostile environment for non-traditional entrants, and women and ethnic minority employees face both similar and different challenges and attitudinal barriers. Discriminatory behavior perpetrated by the dominant white male workforce is commonplace, as are informal recruitment practices, exclusive networks and a competitive and adversarial culture. By comparing and analyzing the results and recommendations from the two studies, the paper identifies where action is required to lead to a more balanced and socially representative workforce in the future. The similarity of the recommendations put forward by these studies suggests that there would be an advantage in developing a more holistic approach towards managing diversity in the sector. It is argued that addressing a broad range of equal opportunities in an integrated and strategic manner would enhance opportunities for women, ethnic minorities, and for the workforce as a whole.

Periodontitis is closely related to many systemic diseases linked by different periodontal pathogens. To unravel the relationship between periodontitis and systemic diseases, it is very important to correctly discriminate major periodontal pathogens. To realize convenient, efficient, and high-accuracy bacterial species classification, the authors use Raman spectroscopy combined with machine learning algorithms to distinguish three major periodontal pathogens Porphyromonas gingivalis (Pg), Fusobacterium nucleatum (Fn), and Aggregatibacter actinomycetemcomitans (Aa). The result shows that this novel method can successfully discriminate the three above-mentioned periodontal pathogens. Moreover, the classification accuracies for the three categories of the original data were 94.7% at the sample level and 93.9% at the spectrum level by the machine learning algorithm extra trees. This study provides a fast, simple, and accurate method which is very beneficial to differentiate periodontal pathogens.

Labor market discrimination is very difficult to pinpoint, even more difficult to measure and almost impossible to “prove”. This paper reviews the literature and makes two main contributions: first, it builds a four-fold typology to think about discrimination — overt or covert; conscious or unconscious; legal or illegal and real or perceived. Second, it identifies screens and filters — devices through which discrimination plays out in the labor market. Unless more empirical studies identify the play of discrimination and exclusion, subordinate groups may well be told that discrimination is actually in their heads — that they are imagining it.

Within the themes of CASCADE NET, this paper focusses on less heard voices and the need to develop new social spaces. Disaster vulnerability identifies diversity in society through a lens of constraints to solutions, on such bases as demography, socio-economic status, cultural, ethnic and gendered minorities within society, and marginalized groups as well as physical proximity to a hazard. The focus of disaster risk reduction is on building resilience through the strengths and capacities in society, but it has a tendency to homogenize characteristics of resilience to the community level, thereby flattening and hiding diversity. LGBTQ people are largely ignored as minority groups with specific information needs. Specific response and recovery processes and actors exacerbate the vulnerability of the LGBTQ minority, especially in evacuation, support, counselling, and rehousing. The role of faith-based organizations (FBO) in providing these services during disaster relief and recovery is examined in this paper. This paper identifies and critiques the attitudes and practices of some FBO towards LGBTQ groups in their provision of disaster relief services.

Developed countries, led by the United States and the European Union, have stepped up security review of supply chains and adopted a series of restrictive policies and protectionist measures to reshape a more secure, sustainable, and risk-controllable supply chain. A key objective of the West’s supply chain strategy is to wean their economies off Chinese influence by resorting to discriminatory policies. Supply chain interventionism on national security grounds violates market rules on which the global supply chain is based, and also runs counter to the principles of nondiscrimination and liberalization embedded in the multilateral trade governance architecture. Global supply chain reshuffles will take time and incur huge costs, leaving ample room for Beijing to make necessary adjustments and bolster its position in global supply networks.

There has been a surge in concern about political polarization and discrimination based on religious and political attitudes in the workplace. Using a nationally-representative sample of nearly 3,000 respondents, this paper presents new empirical evidence on the incidence of social hostilities in the workplace and the causal effects of providing information on consumer and labor market behavior across a range of indicators. Several empirical patterns emerge. First, nearly half of respondents have not shared their personal views about a social or political issue because of fear that sharing it would harm their career. Second, roughly a fifth of respondents have encountered discrimination for respectfully communicating their religious or political viewpoint. Third, these attitudes are negatively correlated with labor market and social media behavior: 40% of respondents say that perceptions of hostility against religious or political views make them much less likely to apply to a company, and 50% of respondents say that they are “very concerned” or “somewhat concerned” about adverse effects related with posting on social media about their religious and political attitudes. Finally, exposure to information about politicized topics is associated with a decline in respondents’ support for companies taking more activist stances. These results suggest that there are adverse and unintended consequences associated with religious and political discrimination, and companies could realize additional benefits by focusing on their core business activities.

China is well known for its “One-Child Policy” introduced in 1979, which intended to curtail the growing population rate. In 2015, it was replaced by a “Two-Child Policy” and numerous new regulations to support families to have two children. The new policy is not as successful as planned, since many Chinese women decide against a second child. The main reason is their fear of facing discrimination at their workplaces and negative effects on their careers. This case discussed the effect of the new policy and whether the two-child policy has contributed to gender discrimination in China.