Working Hours Reduction and Endogenous Growth

This paper formulates an endogenous growth model and uses it to inquire into the long-run impact of work-sharing arrangements on economic growth. We show that the styles of wage contract, namely salary-style and hourly-style contracts, are a key factor in determining the long-run growth effects of working time reduction. If the labor market is overwhelmingly salaried arrangement, then the extent of wage flexibility is relatively low; as a consequence, a policy of reducing working hours will deteriorate economic growth. On the contrary, if hourly pay predominates, then the wage system tends to increase the degree of wage flexibility. Thus, a cut in working time may favor the economy’s growth rate.Working hours reduction, Endogenous growth

MENTOR: Multilingual tExt detectioN TOward leaRning by analogy

Text detection is frequently used in vision-based mobile robots when they need to interpret texts in their surroundings to perform a given task. For instance, delivery robots in multilingual cities need to be capable of doing multilingual text detection so that the robots can read traffic signs and road markings. Moreover, the target languages change from region to region, implying the need of efficiently re-training the models to recognize the novel/new languages. However, collecting and labeling training data for novel languages are cumbersome, and the efforts to re-train an existing/trained text detector are considerable. Even worse, such a routine would repeat whenever a novel language appears. This motivates us to propose a new problem setting for tackling the aforementioned challenges in a more efficient way: "We ask for a generalizable multilingual text detection framework to detect and identify both seen and unseen language regions inside scene images without the requirement of collecting supervised training data for unseen languages as well as model re-training". To this end, we propose "MENTOR", the first work to realize a learning strategy between zero-shot learning and few-shot learning for multilingual scene text detection.Comment: 8 pages, 4 figures, published to IROS 202

The Study of Maximizing Customer Equity by Segmentation: A Modified K-Means Approach

As segmentation has been one of the central marketing tasks for decades and customer profitability valuation has seen wide study during the past few years, surprisingly, up to this date, there is a gap in marketing research that await a bridge to link up of these two important and closely related dimensions. In this paper, we introduce a decision support system with the goal of maximizing customer equity by segmentation. The decision support system introduced here is unique in that it accommodates the essence of customer profitability valuation into a segmentation scheme in a sensible and flexible manner, that it suggests the number of segments to be determined by the goal of profit maximization instead of some arbitrary numerical criterion, and that central to its technical core the outlier problem which is pervasive in cluster analysis has been addressed by a modified K-Means algorithm so that clustering can reflect the pattern of the majority of ordinary observations in a data set instead of being influenced by a handful of outliers. It followed by a number of test datasets from a public data source and a conclusion remark was made at the end

Development and Validations of a 3-D Numerical Wave Model in Cartesian Grid System Using Level Set Method

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive