TY - JOUR
AB - Voltage-activated Ca(2+) channels (VACCs) mediate Ca(2+) influx to trigger action potential-evoked neurotransmitter release, but the mechanism by which Ca(2+) regulates spontaneous transmission is unclear. We found that VACCs are the major physiological triggers for spontaneous release at mouse neocortical inhibitory synapses. Moreover, despite the absence of a synchronizing action potential, we found that spontaneous fusion of a GABA-containing vesicle required the activation of multiple tightly coupled VACCs of variable type.
AU - Williams, Courtney
AU - Chen, Wenyan
AU - Lee, Chia
AU - Yaeger, Daniel
AU - Vyleta, Nicholas
AU - Smith, Stephen
ID - 3121
IS - 9
JF - Nature Neuroscience
TI - Coactivation of multiple tightly coupled calcium channels triggers spontaneous release of GABA
VL - 15
ER -
TY - CONF
AB - We introduce the idea of using an explicit triangle mesh to track the air/fluid interface in a smoothed particle hydrodynamics (SPH) simulator. Once an initial surface mesh is created, this mesh is carried forward in time using nearby particle velocities to advect the mesh vertices. The mesh connectivity remains mostly unchanged across time-steps; it is only modified locally for topology change events or for the improvement of triangle quality. In order to ensure that the surface mesh does not diverge from the underlying particle simulation, we periodically project the mesh surface onto an implicit surface defined by the physics simulation. The mesh surface gives us several advantages over previous SPH surface tracking techniques. We demonstrate a new method for surface tension calculations that clearly outperforms the state of the art in SPH surface tension for computer graphics. We also demonstrate a method for tracking detailed surface information (like colors) that is less susceptible to numerical diffusion than competing techniques. Finally, our temporally-coherent surface mesh allows us to simulate high-resolution surface wave dynamics without being limited by the particle resolution of the SPH simulation.
AU - Yu, Jihun
AU - Wojtan, Christopher J
AU - Turk, Greg
AU - Yap, Chee
ID - 3123
IS - 2
T2 - Computer Graphics Forum
TI - Explicit mesh surfaces for particle based fluids
VL - 31
ER -
TY - CONF
AB - We consider the problem of inference in a graphical model with binary variables. While in theory it is arguably preferable to compute marginal probabilities, in practice researchers often use MAP inference due to the availability of efficient discrete optimization algorithms. We bridge the gap between the two approaches by introducing the Discrete Marginals technique in which approximate marginals are obtained by minimizing an objective function with unary and pairwise terms over a discretized domain. This allows the use of techniques originally developed for MAP-MRF inference and learning. We explore two ways to set up the objective function - by discretizing the Bethe free energy and by learning it from training data. Experimental results show that for certain types of graphs a learned function can outperform the Bethe approximation. We also establish a link between the Bethe free energy and submodular functions.
AU - Korc, Filip
AU - Kolmogorov, Vladimir
AU - Lampert, Christoph
ID - 3124
TI - Approximating marginals using discrete energy minimization
ER -
TY - CONF
AB - We propose a new learning method to infer a mid-level feature representation that combines the advantage of semantic attribute representations with the higher expressive power of non-semantic features. The idea lies in augmenting an existing attribute-based representation with additional dimensions for which an autoencoder model is coupled with a large-margin principle. This construction allows a smooth transition between the zero-shot regime with no training example, the unsupervised regime with training examples but without class labels, and the supervised regime with training examples and with class labels. The resulting optimization problem can be solved efficiently, because several of the necessity steps have closed-form solutions. Through extensive experiments we show that the augmented representation achieves better results in terms of object categorization accuracy than the semantic representation alone.
AU - Sharmanska, Viktoriia
AU - Quadrianto, Novi
AU - Lampert, Christoph
ID - 3125
IS - PART 5
TI - Augmented attribute representations
VL - 7576
ER -
TY - CONF
AB - When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. However, it turns out that the inconsistencies introduced when different graph instances have different edge sets pose a serious challenge. In this work we address this challenge for the problem of finding maximum weighted cliques.
We introduce the concept of most persistent soft-clique. This is subset of vertices, that 1) is almost fully or at least densely connected, 2) occurs in all or almost all graph instances, and 3) has the maximum weight. We present a measure of clique-ness, that essentially counts the number of edge missing to make a subset of vertices into a clique. With this measure, we show that the problem of finding the most persistent soft-clique problem can be cast either as: a) a max-min two person game optimization problem, or b) a min-min soft margin optimization problem. Both formulations lead to the same solution when using a partial Lagrangian method to solve the optimization problems. By experiments on synthetic data and on real social network data, we show that the proposed method is able to reliably find soft cliques in graph data, even if that is distorted by random noise or unreliable observations.
AU - Quadrianto, Novi
AU - Lampert, Christoph
AU - Chen, Chao
ID - 3127
T2 - Proceedings of the 29th International Conference on Machine Learning
TI - The most persistent soft-clique in a set of sampled graphs
ER -