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As for the classification network that is constantly emerging with each passing day, different classification network as the backbone of the semantic segmentation network may show different performance. This paper selected the road extraction data set of CVPR DeepGlobe, and compared the performance differences of VGG-16 as the backbone of Unet, ResNet34, ResNet101 and Xception as the backbone of AD-LinkNet. When VGG-16 is used as the backbone of the semantic segmentation network, it performs better in the face of long and wide road extraction. As the backbone of the semantic segmentation network, ResNet has a higher ability to extract small roads. When Xception is used as the backbone of the semantic segmentation network, it not only retains the characteristics of ResNet34, but also can effectively deal with the complex situation of extracting target covered by occlusions.

Rent-based accounts of inequality argue that institutionalized barriers to the access to labour market positions create artificial restrictions on the supply of labour and, in turn, generate wages for workers in protected positions in excess of the wages they would receive in a competitive labour market. In this article, we extend this argument to the comparative context, and elaborate a rent-based explanation of between-occupation wage inequality in Germany and the United Kingdom. We test it with new and unique data on four institutionalized sources of closure (educational credentialing, licensure, unionization, and apprenticeships), matched to newly constructed measures of occupational skills and to national labour force survey data. We show that in both countries, between-occupation wage inequality is substantial, and much of it can be traced to variations across occupations in closure and to the positive association between closure and wages. We also show that the prevalence and the payoff to each of the four closure institutions differ across the two countries: Specifically, vocational credentialing and unionization have a particularly high payoff in Germany, while tertiary credentialing and licensure have a particularly high payoff in the United Kingdom. These results have important implications for understanding between-occupation wage inequality and cross-national differences in aggregate levels of wage inequality.

Introduction Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques. Method This research uses a hybrid deep learning model that combines two cutting‐edge approaches: Mask Region‐based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset. Results The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state‐of‐the‐art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well. Conclusion In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.

While many advocate 'lifelong learning' as the ideal career model, its impact on workers' lives is still partly unclear. Especially research on monetary returns to further education has yielded mixed evidence. I argue that a thorough assessment has to consider both the types of courses and the segmentation of labour markets. Using data from the German National Educational Panel Study, I test explanations of differing returns to non-formal further education in Germany, a country known for its highly segmented labour market. Results confirm that the returns to short non-formal training courses, which are the most common forms of further education in Germany, differ remarkably between types of courses and segments. Employer-mandated courses yield the highest returns, which is especially pronounced in internal labour markets. Furthermore, there are no returns on closed occupational labour markets. In occupations, where formal credentials are less important, returns to training are present. These results suggest that returns depend less on individual decisions to invest in training and more on the context. Hence, these findings go against human capital explanations and instead support implications of the Job Competition Model and Credentialism, which emphasize the importance of labour market structure.

US cities today are vastly more educated and skill-intensive than they were five decades ago. Yet, urban non-college workers perform substantially less skilled jobs than decades earlier. This deskilling reflects the joint effects of automation and, secondarily, rising international trade, which have eliminated the bulk of non-college production, administrative support, and clerical jobs, yielding a disproportionate polarization of urban labor markets. The unwinding of the urban non-college occupational skill gradient has, I argue, abetted a secular fall in real non-college wages by: (1) shunting non-college workers out of specialized middle-skill occupations into low-wage occupations that require only generic skills; (2) diminishing the set of non-college workers that hold middle-skill jobs in high-wage cities; and (3) attenuating, to a startling degree, the steep urban wage premium for non-college workers that prevailed in earlier decades. Changes in the nature of work--many of which are technological in origin--have been more disruptive and less beneficial for non-college than college workers.

Above‐ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot‐wise tree volume bias is ±0.4 m3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step. The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open‐source software.

Using IPUMS data for five decennial years between 1970 and 2010, we delineate and compare the trends and sources of the racial pay gap among men and women in the U.S. labor force. Decomposition of the pay gap into components underscores the significance of the intersection between gender and race; we find meaningful gender differences in the composition of the gap and in the gross and the net earnings gaps—both are much larger among men than among women. Despite these differences, the over-time trend is strikingly similar for both genders. Racial gaps sharply declined between 1970 and 1980 and continued to decline, but at a slower rate, until 2000. However, at the turn of the millennium, the trend reversed for both gender groups. The growth of the racial pay gap at the turn of the millennium is attributable to the increase in overall income inequality, stagnation in occupational segregation, and an increase in the unexplained portion of the gap, a portion we attribute to economic discrimination.

Summary Machine learning use in plant phenotyping has grown exponentially. These algorithms empowered the use of image data to measure plant traits rapidly and to predict the effect of genetic and environmental conditions on plant phenotype. However, the lack of interpretability in machine learning models has limited their usefulness in gaining insights into the underlying biological processes that drive plant phenotypes. Explainable AI (XAI) emerges to help understand the ‘why’ behind machine learning model predictions and allow researchers to investigate the most influential features that lead to prediction, classification or segmentation results. Understanding the mechanisms behind model prediction is also central to sanity‐checking models, increasing model reliability and identifying dataset biases that may limit the model's applicability across different conditions. This review introduces the concept of XAI and presents current algorithms, emphasizing their suitability for different data types or machine learning algorithms. The use of XAI to leverage trait information is highlighted, showcasing how recent studies employed model explanations to recognize the features that impact plant phenotype. Overall, this review presents a framework for using XAI to gain insights into intricate biological processes driving plant phenotypes, underscoring the significance of transparency and interpretability in machine learning.

Passive acoustic monitoring is usually presented as a complementary approach to monitoring wildlife communities and assessing ecosystem conditions. Automatic species detection methods support biodiversity monitoring and analysis by providing information on the presence–absence of species, which allows understanding the ecosystem structure. Therefore, different alternatives have been proposed to identify species. However, the algorithms are parameterized to identify specific species. Analysing multiple species would help to monitor and quantify biodiversity, as it includes the different taxonomic groups present in the soundscape. We present an unsupervised methodology for multi‐species call recognition from ecological soundscapes. The proposal is based on a clustering algorithm, specifically the learning algorithm for multivariate data analysis (LAMDA) 3pi algorithm, which automatically suggests the number of clusters associated with the sonotypes. Emphasis was made on improving the segmentation of the audio to analyse the whole soundscape without parameterizing the algorithm according to each taxonomic group. To estimate the performance of our proposal, we used four datasets from different locations, years and habitats. These datasets contain sounds from the four major taxonomic groups that dominate terrestrial soundscapes (birds, amphibians, mammals and insects) in audible and ultrasonic spectra. The methodology presents performances between 75% and 96% in presence–absence species recognition. Using the clusters proposed by our methodology, the whole soundscape biodiversity was measured and compared with the estimate of four acoustic indices (ACI, NP, SO and BI). Our approach performs biodiversity assessments similar to acoustic indices with the advantage of providing information about acoustic communities without the need for prior knowledge of the species present in the audio recordings.

The international literature hypothesized a “U-shaped” pattern of immigrants’ occupational trajectories from origin to destination countries due to the imperfect transferability of human capital. However, empirical evidence supporting this hypothesis is available only in single-country studies and for “old,” Anglo-Saxon migration countries with deregulated labor markets. This article compares Italy, Spain, and France, providing evidence that the more segmented the labor market, the higher immigrants’ occupational downgrade on arrival, independently from skills transferability and other individual characteristics. Paradoxically, the more segmented the labor market, the more important the acquisition of host-country specific human capital for subsequent upward mobility.