Research supports an association between depressive symptoms and reduced relationship satisfaction. Yet the etiology of this relationship is unclear. The purpose of this study was to examine individual and partner influence on relationship satisfaction and depressive symptoms. We assessed whether romantic partners (N ¼ 84) reported similar depressive states and whether relationship satisfaction was influenced by partner and/or individual depressive symptoms. Partners had similar levels of depressive symptoms and relationship satisfaction. Actor-partner interdependence models demonstrated no partner effects of depressive symptoms on relationship satisfaction. However, there was an actor effect such that men's depressive symptoms predicted their own relationship satisfaction; thus, how they perceive the romantic relationship. Couples counseling may be an effective form of treatment for individuals suffering from depressive symptoms to include partner support and to unravel how depressive symptoms may interfere with relationship perceptions.

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Telecommunication operators are massively moving their network functions in small data centers at the edge of the network, which are becoming increasingly common. However, the high performance provided by commonly used technologies for data plane processing such as DPDK, based on kernel-bypass primitives, comes at the cost of rigid resource partitioning. This is unsuitable for edge data centers, in which efficiency demands both general-purpose applications and data-plane telco workloads to be executed on the same (shared) physical machines. In this respect, eBPF/XDP looks a more appealing solution, thanks to its capability to process packets in the kernel, achieving a higher level of integration with non-data plane applications albeit with lower performance than DPDK. In this paper we leverage the recent introduction of AF_XDP, an XDP-based technology that allows to efficiently steer packets in user space, to provide a thorough comparison of user space vs in-kernel packet processing in typical scenarios of a data center at the edge of the network. Our results provide useful insights on how to select and combine these technologies in order to improve overall throughput and optimize resource usage.