No abstract

No abstract

While Uzbekistan and Central Asia are known for the well-studied Bronze Age civilization of Bactria-Margiana Archaeological Complex (BMAC), the lesser-known Iron Age was also a dynamic period that resulted in increased interaction and admixture among different cultures from this region. To broaden our understanding of events which impacted the demography and population structure of this region, we generated 27 genome-wide SNP capture datasets of Late Iron Age individuals around the Historical Kushan time period (∼2100-1500 BP) from three sites in South Uzbekistan. Overall, Bronze Age ancestry persists into the Iron Age in Uzbekistan, with no major replacements of populations with Steppe-related ancestry. However, these individuals suggest diverse ancestries related to Iranian farmers, Anatolian farmers and Steppe herders, with a small amount of West European Hunter Gatherer, East Asian and South Asian Hunter Gatherer ancestry as well. Genetic affinity towards the Late Bronze Age Steppe herders and a higher Steppe-related ancestry than that found in BMAC populations suggest an increased mobility and interaction of individuals from Northern Steppe in a Southward direction. In addition, a decrease of Iranian farmers and an increase of Anatolian farmer-like ancestry in Uzbekistan Iron Age individuals were observed compared to the BMAC populations from Uzbekistan. Thus, despite continuity from the Bronze Age, increased admixture played a major role in the shift from the Bronze to the Iron Age in southern Uzbekistan. This mixed ancestry is also observed in other parts of the Steppe and Central Asia, suggesting more widespread admixture among local populations.

Under stereo settings, the problem of image super-resolution (SR) and disparity estimation are interrelated that the result of each problem could help to solve the other. The effective exploitation of correspondence between different views facilitates the SR performance, while the high-resolution (HR) features with richer details benefit the correspondence estimation. According to this motivation, we propose a Stereo Super-Resolution and Disparity Estimation Feedback Network (SSRDE-FNet), which simultaneously handles the stereo image super-resolution and disparity estimation in a unified framework and interact them with each other to further improve their performance. Specifically, the SSRDE-FNet is composed of two dual recursive sub-networks for left and right views. Besides the cross-view information exploitation in the low-resolution (LR) space, HR representations produced by the SR process are utilized to perform HR disparity estimation with higher accuracy, through which the HR features can be aggregated to generate a finer SR result. Afterward, the proposed HR Disparity Information Feedback (HRDIF) mechanism delivers information carried by HR disparity back to previous layers to further refine the SR image reconstruction. Extensive experiments demonstrate the effectiveness and advancement of SSRDE-FNet.