Variational Bayes model averaging for graphon functions and motif frequencies inference in W-graph models

W-graph refers to a general class of random graph models that can be seen as a random graph limit. It is characterized by both its graphon function and its motif frequencies. In this paper, relying on an existing variational Bayes algorithm for the stochastic block models along with the corresponding weights for model averaging, we derive an estimate of the graphon function as an average of stochastic block models with increasing number of blocks. In the same framework, we derive the variational posterior frequency of any motif. A simulation study and an illustration on a social network complete our work

Learning a Hybrid Architecture for Sequence Regression and Annotation

When learning a hidden Markov model (HMM), sequen- tial observations can often be complemented by real-valued summary response variables generated from the path of hid- den states. Such settings arise in numerous domains, includ- ing many applications in biology, like motif discovery and genome annotation. In this paper, we present a flexible frame- work for jointly modeling both latent sequence features and the functional mapping that relates the summary response variables to the hidden state sequence. The algorithm is com- patible with a rich set of mapping functions. Results show that the availability of additional continuous response vari- ables can simultaneously improve the annotation of the se- quential observations and yield good prediction performance in both synthetic data and real-world datasets.Comment: AAAI 201

Detecting and Describing Dynamic Equilibria in Adaptive Networks

We review modeling attempts for the paradigmatic contact process (or SIS model) on adaptive networks. Elaborating on one particular proposed mechanism of topology change (rewiring) and its mean field analysis, we obtain a coarse-grained view of coevolving network topology in the stationary active phase of the system. Introducing an alternative framework applicable to a wide class of adaptive networks, active stationary states are detected, and an extended description of the resulting steady-state statistics is given for three different rewiring schemes. We find that slight modifications of the standard rewiring rule can result in either minuscule or drastic change of steady-state network topologies.Comment: 14 pages, 10 figures; typo in the third of Eqs. (1) correcte

Three-Dimensional Structure of the Complexin/SNARE Complex

During neurotransmitter release, the neuronal SNARE proteins synaptobrevin/VAMP, syntaxin, and SNAP-25 form a four-helix bundle, the SNARE complex, that pulls the synaptic vesicle and plasma membranes together possibly causing membrane fusion. Complexin binds tightly to the SNARE complex and is essential for efficient Ca2+-evoked neurotransmitter release. A combined X-ray and TROSY-based NMR study now reveals the atomic structure of the complexin/SNARE complex. Complexin binds in an antiparallel α-helical conformation to the groove between the synaptobrevin and syntaxin helices. This interaction stabilizes the interface between these two helices, which bears the repulsive forces between the apposed membranes. These results suggest that complexin stabilizes the fully assembled SNARE complex as a key step that enables the exquisitely high speed of Ca2+-evoked neurotransmitter release