Unbiased Model-Agnostic Metalearning Algorithm for Learning Target-Driven Visual Navigation Policy

As deep reinforcement learning methods have made great progress in the visual navigation field, metalearning-based algorithms are gaining more attention since they greatly improve the expansibility of moving agents. According to metatraining mechanism, typically an initial model is trained as a metalearner by existing navigation tasks and becomes well performed in new scenes through relatively few recursive trials. However, if a metalearner is overtrained on the former tasks, it may hardly achieve generalization on navigating in unfamiliar environments as the initial model turns out to be quite biased towards former ambient configuration. In order to train an impartial navigation model and enhance its generalization capability, we propose an Unbiased Model-Agnostic Metalearning (UMAML) algorithm towards target-driven visual navigation. Inspired by entropy-based methods, maximizing the uncertainty over output labels in classification tasks, we adopt inequality measures used in Economics as a concise metric to calculate the loss deviation across unfamiliar tasks. With succinctly minimizing the inequality of task losses, an unbiased navigation model without overperforming in particular scene types can be learnt based on Model-Agnostic Metalearning mechanism. The exploring agent complies with a more balanced update rule, able to gather navigation experience from training environments. Several experiments have been conducted, and results demonstrate that our approach outperforms other state-of-the-art metalearning navigation methods in generalization ability.

The Robin Hood Index Adjusted for Negatives and Equivalised Incomes

Indisputable figures on income and wealth inequality are indispensable for politics, society and science. Although the Gini coefficient is the most common measure of inequality, the straightforward concept of the Robin Hood index (namely, the income share that has to be transferred from the rich to the poor to make everyone equally well off) makes it a more attractive measure for the general public. In a distribution with many negative values – particularly wealth distributions – the Robin Hood index can take on values larger than 1, indicating an intuitively impossible income transfer of more than 100%. This article proposes a method to normalise the Robin Hood index. In contrast to the original index, the normalised Robin Hood index always takes on values between 0 and 1 and ends up as the original index in a distribution without negatives. As inequality measures are commonly applied to equivalised income, we also introduce a method for adequately transferring equivalised incomes from the rich to the poor within the framework of the (normalised) Robin Hood index. An empirical application shows the effect of normalisation for the Robin Hood index, and compares it to the normalisation of the Gini coefficient from previous research.

Post-crisis developments in young adults’ housing wealth

How has housing wealth inequality changed for young-adult households in the post-financial crisis period, and what is driving such change? We chart a path for subsequent studies by analysing the previously unexamined post-crisis housing wealth profile of young adults via different angles and using multiple inequality measures. Using household micro-data for 11 European countries ( Household Finance and Consumption Survey, 2010–2017) and the United States ( Survey of Consumer Finances, 2010–2016), we find that the accumulation of housing assets for 22–44 year olds is unevenly concentrated among high-income homeowners, over and above what would be expected given the well-known decline in homeownership. We describe and assess several potential drivers for these wealth profile changes, finding that the current explanations offered in the literature do not adequately account for the unequal wealth profile of young people. We conclude that a mix of dynamics, including housing market volatility, housing market configurations leading to uneven capital gains and losses, and the increased social selectivity of homeownership intersect to shape the ways that young adults navigate the housing market in post-crisis times.

Spatial Transformation of the Pig Sector in Hungary and Slovenia: A Comparative Analysis

This paper presents a comparative analysis of the spatial transformation in the Hungarian and Slovenian pig sectors at the level of local administrative units (LAU). Concentration and inequality measures were applied in the empirical analyses, along with Markov transition probability matrices, to examine the stability and/or mobility over time and the presence of clustering effects. Both countries experienced a rapid decline in pig population. This profound structural change has led to a smaller number of more concentrated pig farms and increased territorial concentration. The degree of farm and territorial concentration and inequality in Hungary has been much higher than in Slovenia, and the concentration gap between the countries has increased. Between 2000 and 2010, the degree of concentration was much higher in Hungary than in Slovenia; average herd size per holding increased by 68 percent in Hungary, and only seven percent in Slovenia. In Hungary, clustering effects were particularly significant, with the pig sector moving towards large-scale concentration. The former effect was also confirmed in the Slovenian pig sector, but significantly weakened during the period under investigation. The exploitation and policy management of spatial externalities justifies these agricultural, economic, and agri-environmental practices.

A Unified General Class of Interdistributional Income Inequality Measures Based on Weighted Incomplete Moments

Many authors have proposed measures for between groups income inequalities. Mostly, these measures are based on functional of the income distribution. Others are based on Gini index, measures of entropies or additive functions. Butler and McDonald (1987) developed a class of between groups income inequality measures based on incomplete moments and showed its applicability. In this article, A unified class of interdistributional inequality measures are introduced. Most of previous measures are special cases from the new class, such as Butler-McDonald measures. These new measures are estimated and studied. Also, the new general class is based on probability weighted moments which can be given any values as the upper value. A real data application is presented to compare among all these measures and show the benefits of the new measures.

Natural resources and wealth inequality: a cross-country analysis

PurposeThis study investigates the impact of natural resources on wealth inequality as a first attempt on a panel of 45 developed and developing countries.Design/methodology/approachUsing the generalized method of moments (GMM), the results provide strong evidence that natural resources increase wealth inequality within a linear empirical framework.FindingsThese results are robust to the use of alternative natural resources and wealth inequality measures. Additionally, a nonlinear analysis provides evidence of an inverted U shaped relationship between natural resources and wealth inequality. The net effect of enhancing natural resources on wealth inequality is positive and building on the corresponding conditional negative effect, the attendant natural resource thresholds for inclusive development are provided. It follows that while natural resources increase wealth inequality, some critical levels of natural resources are needed for natural resources to reduce wealth inequality.Originality/valueTo the best of knowledge, this is the first study to assess how enhancing natural resources affect wealth inequality.

Using git metrics to measure students' and teams' code contributions in software development projects

Many software engineering courses are centered around team-based project development. Analyzing the source code contributions during the projects’ development could provide both instructors and students with constant feedback to identify common trends and behaviors that can be improved during the courses. Evaluating course projects is a challenge due to the difficulty of measuring individual student contributions versus team contributions during the development. The adoption of distributed version control sys-tems like git enable the measurement of students’ and teams’ contributions to the project.In this work, we analyze the contributions within eight software development projects,with 150 students in total, from undergraduate courses that used project-based learning.We generate visualizations of aggregated git metrics using inequality measures and the contribution per module, which offer insights into the practices and processes followed by students and teams throughout the project development. This approach allowed us to identify inequality among students’ contributions, the modules where students con-tributed, development processes with a non-steady pace, and integration practices render-ing a useful feedback tool for instructors and students during the project’s development.Further studies can be conducted to assess the quality, complexity, and ownership of the contributions by analyzing software artifacts.

Subnational Inequality Divergence

How have inequality levels across local labor markets in the subnational United States changed over the past eight decades? In this study, I examine inequality divergence, or the inequality of inequalities. While divergence trends of central tendencies such as per capita income have been well documented, less is known about the descriptive trends or contributing mechanisms for inequality. In this study, I construct wage inequality measures in 722 local labor markets covering the entire contiguous United States across 22 waves of Census and American Community Survey data from 1940-2019 to assess the historical trends of inequality divergence. I apply variance decomposition and counterfactual techniques to develop main conclusions. Inequality divergence follows a u-shaped pattern, declining through 1990 but with contemporary divergence at as high a level as any time in the past 80 years. Early era convergence occurred broadly and primarily worked to reduce interregional differences, whereas modern inequality divergence operates through a combination of novel mechanisms, most notably through highly unequal urban areas separating from other labor markets. Overall, results show geographical fragmentation of inequality underneath overall inequality growth in recent years, highlighting the fundamental importance of spatial trends for broader stratification outcomes.

On The Transmuted Powered Moment Exponential Distribution

We introduce a new class of lifetime models called the transmuted powered moment exponential distribution. More specifically, the transmuted powered moment exponential distribution covers several new distributions. Survival analysis including survival function, hazard rate function and other related measures are computed. Analytical expressions for various mathematical properties of TPMED including rth moment, quantile function, inequality measures, and parameters are estimated by using maximum likelihood estimation and order statistics are also derived. A simulation study of the proposed distribution is performed. It is discovered that the Maximum Likelihood Estimators are consistent since the bias and Mean Square Error approach to zero when the sample size increases. The usefulness of the model associated with this distribution is illustrated by two real data sets and the new model provides a better fit than the models provided in literature.