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Background: As phenotypes of depressive disorders (DD) are highly heterogenous, a growing number of studies investigate person-specific associations of depressive symptoms in time series data. Most available methods for estimating applicable models rely on the assumption that the associations between variables stay constant over time, which can be unrealistic in clinical contexts. To circumvent this limitation, we used a recently developed technique to estimate time-varying vector autoregressive models. Methods: In daily diary data of 20 participants with DD with a mean length of 274 days (SD = 82.4, range = 154-539), we modeled idiographic associations between core depressive symptoms, rumination, sleep, and quantity and quality of social contacts as idiographic time-varying dynamical networks. Results: Resulting models showed marked inter- as well as intraindividual differences. For some participants, associations between variables changed fast over time, whereas for others they showed more stability. Our results further indicated nonstationarity in all time series. Discussion: Idiographic symptom networks of depression can be of interest to clinicians and researchers as they can capture changes over time and provide detailed insights into the temporal course of mental disorders. Whilst the assumption of stationarity can hinder insights into important change processes, time-varying network models are a promising approach. We discuss limitations, their possible solutions, and recommendations for further use of the modeling technique.

IaaS clouds have become a promising platform for scalable distributed systems in recent years. However, while the virtualization techniques of such clouds are key to the cloud's elasticity, they also result in a reduced and less predictable I/O performance compared to traditional HPC setups. Besides the regular performance degradation of virtualized I/O itself, it is also the potential loss of I/O bandwidth through co-located virtual machines that imposes considerable obstacles for porting dataintensive applications to that platform.In this paper we examine adaptive compression schemes as a means to mitigate the negative effects of shared I/O in IaaS clouds. We discuss the decision models of existing schemes and analyze their applicability in virtualized environments. Based on an evaluation using XEN, KVM, and Amazon EC2, we found that most decision metrics (like CPU utilization and I/O bandwidth) are displayed inaccurately inside virtual machines and can lead to unreasonable levels of compression. As a remedy, we present a new adaptive compression scheme for virtualized environments which solely considers the application data rate. Without requiring any calibration or training phase our adaptive compression scheme can improve the I/O throughput of virtual machines significantly as shown through experimental evaluation.

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