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In this paper, we use quarterly stock price index data to test the weak form of the efficient market hypothesis for 13 art market indices. The fact that hypotheses can be efficiently tested by performing unit root tests is well known. The conventional unit root test results show that most art indices have unit roots, which means that these art markets are efficient. However, the results of the non-linear quantile unit root tests we conduct over a range of quantiles lead us to reject the null of the unit root test for several art markets. To acquire more information, we perform the test over sub-quantile intervals, finding asymmetric market efficiency between the low and high quantile intervals: we cannot reject the unit root null in upper quantile intervals for most art price indices. [ABSTRACT FROM AUTHOR]

In the context of LiDAR sensor-based autonomous vehicles, segmentation networks play a crucial role in accurately identifying and classifying objects. However, discrepancies between the types of LiDAR sensors used for training the network and those deployed in real-world driving environments can lead to performance degradation due to differences in the input tensor attributes, such as x, y, and z coordinates, and intensity. To address this issue, we propose novel intensity rendering and data interpolation techniques. Our study evaluates the effectiveness of these methods by applying them to object tracking in real-world scenarios. The proposed solutions aim to harmonize the differences between sensor data, thereby enhancing the performance and reliability of deep learning networks for autonomous vehicle perception systems. Additionally, our algorithms prevent performance degradation, even when different types of sensors are used for the training data and real-world applications. This approach allows for the use of publicly available open datasets without the need to spend extensive time on dataset construction and annotation using the actual sensors deployed, thus significantly saving time and resources. When applying the proposed methods, we observed an approximate 20% improvement in mIoU performance compared to scenarios without these enhancements. [ABSTRACT FROM AUTHOR]

This study tests for the weak-form market efficiency of 15 cryptocurrency prices. The conventional unit root tests and stationary test results reveal that most cryptocurrency markets are efficient markets. However, the non-linear quantile unit root test proposed by Li and Park (2018) rejects the unit root null hypothesis over the whole quantile level. To derive more informative ideas, we split the whole quantile interval to several sub-intervals and find asymmetric behaviour of the market efficiency across the lower and upper sub-intervals in several cryptocurrency markets. Moreover, non-linear quantile unit root tests for Chainlink, Bitcoin Cash, Binance Coin, EOS, Tron, and Stellar indicate that markets for these cryptocurrencies are efficient at the upper sub-intervals. [ABSTRACT FROM AUTHOR]

This study uses the Vietnam Access to Resources Household Survey (VARHS) data from 2008 to 2014 to analyse the relationship between socio-political capital and household income in rural Vietnam. We consider six types of socio-political capital (connections with others that create access to useful information and/or benefits) namely Communist Party membership, membership in mass organizations (MOs) and voluntary groups, connections with officials, financial assistance, and trust. Communist Party membership and financial assistance have significant positive effects on household income in the pooling, random, and fixed effects models and at all quantile levels. The effect of trust is also positive and significant in all models but only significant at high quantile levels. Whereas connections with officials show significantly positive effects in the pooling and random effects models and at most quantile levels, the effects of MOs and voluntary groups are negative and significant for some models. [ABSTRACT FROM AUTHOR]