Immigrants'assimilation process in a segmented labor market

While much of the literature on immigrants' assimilation has focused on countries with a large tradition of receiving immigrants and with flexible labor markets, very little is known on how immigrants adjust to other types of host economies. With its severe dual labor market, and an unprecedented immigration boom, Spain presents a quite unique experience to analyze immigrations' assimilation process. Using data from the 2000 to 2008 Labor Force Survey, we find that immigrants are more occupationally mobile than natives, and that much of this greater flexibility is explained by immigrants' assimilation process soon after arrival. However, we find little evidence of convergence, especially among women and high skilled immigrants. This suggests that instead of integrating, immigrants occupationally segregate, providing evidence consistent with both imperfect substitutability and immigrants' human capital being under-valued. Additional evidence on the assimilation of earnings and the incidence of permanent employment by different skill levels also supports the hypothesis of segmented labor markets.

The Part-time pay penalty in a segmented labor market

This paper is the first to examine the implications of switching to PT work for women's subsequent earnings trajectories, distinguishing by their type of contract: permanent or fixedterm. Using a rich longitudinal Spanish data set from Social Security records of over 76,000 prime-aged women strongly attached to the Spanish labor market, we find that PT work aggravates the segmentation of the labor market insofar there is a PT pay penalty and this penalty is larger and more persistent in the case of women with fixed-term contracts. The paper discusses problems arising in empirical estimation (including a problem not discussed in the literature up to now: the differential measurement error of the LHS variable by PT status), and how to address them. It concludes with policy implications relevant for Continental Europe and its dual structure of employment protection.

An alternative method for dating unknown tephras based on a segmented regression model

In CoDaWork’05, we presented an application of discriminant function analysis (DFA) to 4 differentcompositional datasets and modelled the first canonical variable using a segmented regression modelsolely based on an observation about the scatter plots. In this paper, multiple linear regressions areapplied to different datasets to confirm the validity of our proposed model. In addition to dating theunknown tephras by calibration as discussed previously, another method of mapping the unknown tephrasinto samples of the reference set or missing samples in between consecutive reference samples isproposed. The application of these methodologies is demonstrated with both simulated and real datasets.This new proposed methodology provides an alternative, more acceptable approach for geologists as theirfocus is on mapping the unknown tephra with relevant eruptive events rather than estimating the age ofunknown tephra.Kew words: Tephrochronology; Segmented regression

SLAM using an Imaging Sonar for Partially Structured Underwater Environments

In this paper we describe a system for underwater navigation with AUVs in partially structured environments, such as dams, ports or marine platforms. An imaging sonar is used to obtain information about the location of planar structures present in such environments. This information is incorporated into a feature-based SLAM algorithm in a two step process: (I) the full 360deg sonar scan is undistorted (to compensate for vehicle motion), thresholded and segmented to determine which measurements correspond to planar environment features and which should be ignored; and (2) SLAM proceeds once the data association is obtained: both the vehicle motion and the measurements whose correct association has been previously determined are incorporated in the SLAM algorithm. This two step delayed SLAM process allows to robustly determine the feature and vehicle locations in the presence of large amounts of spurious or unrelated measurements that might correspond to boats, rocks, etc. Preliminary experiments show the viability of the proposed approach

Application of discriminant function analysis and change-point problem in dating volcanic ashes

The application of Discriminant function analysis (DFA) is not a new idea in the studyof tephrochrology. In this paper, DFA is applied to compositional datasets of twodifferent types of tephras from Mountain Ruapehu in New Zealand and MountainRainier in USA. The canonical variables from the analysis are further investigated witha statistical methodology of change-point problems in order to gain a betterunderstanding of the change in compositional pattern over time. Finally, a special caseof segmented regression has been proposed to model both the time of change and thechange in pattern. This model can be used to estimate the age for the unknown tephrasusing Bayesian statistical calibration

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

Characterization of alkaline lignins for use in penol-formaldehyde and epoxy resins

Besides polyurethanes and polyesters, phenolic and epoxy resins are the most prominent applications for technical lignins in thermosetting materials. To evaluate the potential application of lignin raw materials in phenol formaldehyde and epoxy resins, three types of alkaline lignins were characterized in terms of their structures and thermal properties. The lignin samples analyzed were kraft lignin (LIG-1), soda–rice straw lignin (LIG-2), and soda-wheat straw lignin (LIG-3). FTIR and 1H-NMR methods were used to determine their structure. Gel permeation chromatography (GPC) was used to determine the molecular weight distribution (MWD). Differential scanning calorimetry (DSC) was used to measure the glass transition temperature (Tg), and thermogravimetric analysis (TGA) to determine the thermal stability of lignin samples. Results showed that kraft lignin (LIG-1) has moderate hydroxyl-group content, is rich in G-type units, and has good thermal stability. These properties make it more suitable for direct use in phenol formaldehyde resins, and it is therefore a good raw material for this purpose. The alkaline soda-rice straw lignin (LIG-2) with a high hydroxyl-group content and excellent thermal stability is most suited to preparing lignin-based epoxy resin

Wage inequality and unemployment with overeducation

A skill-biased change in technology can account at once for the changes observed in a number of important variables of the US labour market between 1970 and 1990. These include the increasing inequality in wages, both between and within education groups, and the increase in unemployment at all levels of education. In contrast, in previous literature this type of technology shock cannot account for all of these changes. The paper uses a matching model with a segmented labour market, an imperfect correlation between individual ability and education, and a fixed cost of setting up a job. The endogenous increase in overeducation is key to understand the response of unemployment to the technology shock.

Structural unemployment

Structural unemployment is due to mismatch between available jobs and workers.We formalize this concept in a simple model of a segmented labor market with searchfrictions within segments. Worker mobility, job mobility and wage bargaining costsacross segments generate structural unemployment. We estimate the contribution ofthese costs to fluctuations in US unemployment, operationalizing segments as statesor industries. Most structural unemployment is due to wage bargaining costs, whichare large but nevertheless contribute little to unemployment fluctuations. Structuralunemployment is as cyclical as overall unemployment and no more persistent, bothin the current and in previous recessions.

A knowledge based signal processing approach to tonic identification in indian classical music

In this paper, we describe several techniques for detecting tonic pitch value in Indian classical music. In Indian music, the raga is the basic melodic framework and it is built on the tonic. Tonic detection is therefore fundamental for any melodic analysis in Indian classical music. This workexplores detection of tonic by processing the pitch histograms of Indian classic music. Processing of pitch histograms using group delay functions and its ability to amplify certain traits of Indian music in the pitch histogram, is discussed. Three different strategies to detect tonic, namely, the concert method, the template matching and segmented histogram method are proposed. The concert method exploits the fact that the tonic is constant over a piece/concert.templatematchingmethod and segmented histogrammethodsuse the properties: (i) the tonic is always present in the background, (ii) some notes are less inflected and dominant, to detect the tonic of individual pieces. All the three methods yield good results for Carnatic music (90−100% accuracy), while for Hindustanimusic, the templatemethod works best, provided the v¯adi samv¯adi notes for a given piece are known (85%).

Les TIC i les transformacions de l'empresa catalana. Informe de recerca II

El projecte de recerca Les tecnologies de la informació i la comunicació i les transformacions de l'empresa catalana va analitzar la relació entre l'estratègia, l'organització i les pràctiques empresarials i els usos de les tecnologies de la informació i la comunicació a l'empresa catalana. A partir d'una enquesta a una mostra representativa a 2.038 empreses catalanes, segmentada per branques d'activitat i per dimensió, aquesta recerca va obtenir evidència concloent del procés de construcció de l'empresa xarxa a Catalunya, com també de la relació entre la inversió i els usos de la innovació digital i el seu impacte favorable sobre la productivitat, els salaris i la competitivitat internacional de les empreses catalanes.

A novel semi-fragile forensic watermarking scheme for remote sensing images

Peer-reviewed

Beneficial effects of word final stress in segmenting a new language: evidence from ERPs

Background: How do listeners manage to recognize words in an unfamiliar language? The physical continuity of the signal, in which real silent pauses between words are lacking, makes it a difficult task. However, there are multiple cues that can be exploited to localize word boundaries and to segment the acoustic signal. In the present study, word-stress was manipulated with statistical information and placed in different syllables within trisyllabic nonsense words to explore the result of the combination of the cues in an online word segmentation task. Results: The behavioral results showed that words were segmented better when stress was placed on the final syllables than when it was placed on the middle or first syllable. The electrophysiological results showed an increase in the amplitude of the P2 component, which seemed to be sensitive to word-stress and its location within words. Conclusion: The results demonstrated that listeners can integrate specific prosodic and distributional cues when segmenting speech. An ERP component related to word-stress cues was identified: stressed syllables elicited larger amplitudes in the P2 component than unstressed ones.

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal