Title:Suppression of epidemic spreading in time-varying multiplex networks
Speaker: Lifeng Shen
Suppressing and preventing epidemic spreading is of critical importance to the well being of the human society. To uncover phenomena that can guide control and management of epidemics is thus of significant value. An understanding of epidemic spreading dynamics in the real world requires the following two ingredients. Firstly, a multiplex network de- scription is necessary, because information diffusion in the virtual communication layer of the individuals can affect the disease spreading dynamics in the physical contact layer, and vice versa. The interaction between the dynamical processes in the two layers is typically asymmetric. Secondly, both network layers are in general time varying. In spite of the large body of literature on spreading dynamics in complex networks, the effect of the asymmetrical interaction between information diffusion and epidemic spreading in activity-driven, time-varying multiplex networks have not been understood. We address this problem by developing a general theory based on the approach of microscopic Markov chain, which enables us to predict the epidemic threshold and the final infection density in the physical layer, on which the information diffusion process in the virtual layer can have a significant effect. The focus of our study is on uncovering and understanding mechanisms to inhibit physical disease spreading. We find that stronger heterogeneity in the individual activities and a smaller contact capacity in the communication layer can promote the inhibitory effect. A remarkable phenomenon is that an enhanced positive correlation between the activities in the two layers can greatly suppress the spreading dynamics, suggesting a practical and effective approach to controlling epidemics in the real world.
Supervisor: Cheng Yang
Title:Automated Design of Deep Learning Methods for Biomedical Image Segmentation
Speaker: Hao Tan
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithms enable 3D image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We propose nnU-Net, a deep learning framework that condenses the current domain knowledge and autonomously takes the key decisions required to transfer a basic architecture to different datasets and segmentation tasks. Without manual tuning, nnU-Net surpasses most specialized deep learning pipelines in 19 public international competitions and sets a new state of the art in the majority of the 49 tasks. The results demonstrate a vast hidden potential in the systematic adaptation of deep learning methods to different datasets. We make nnU-Net publicly available as an open-source tool that can effectively be used out-of-the-box, rendering state of the art segmentation accessible to non-experts and catalyzing scientific progress as a framework for automated method design.
Supervisor: Li Gou