We propose a particle-based distributed PHD filter for tracking the states of an unknown, time-varying number targets. To reduce communication, local filters at neighboring sensors communicate Gaussian mixture (GM) parameters. In contrast to most existing filters, our employs “arithmetic average” fusion. For particles–GM conversion, we use method that avoids particle clustering and enables sign...

The learning capability of a neural network improves with increasing depth at higher computational costs. Wider layers with dense kernel connectivity patterns further increase this cost and may hinder real-time inference. We propose feature map and kernel pruning for reducing the computational complexity of a deep convolutional neural network. Due to coarse nature, these pruning granularities c...

The search for the optimal word sequence can be performed efficiently even in a speech recognizer with a very large vocabulary and complex models. This is achieved using pruning methods with empirically chosen parameters and the willingness to accept a certain amount of pruning errors. Quite unsatisfying though, it is state-of-the-art that such pruning errors are not directly detected but, inst...

Climate change is known as one of the most important environmental challenges. Sequestration of carbon in terrestrial ecosystems is a low-cost option that may be available in the near-term to mitigate increasing atmospheric CO2 concentrations, while providing additional benefits. In this study, we estimated the effects of planting density and grazing intensity on the potential of Atriplex canes...

Node pruning is a commonly used technique for solution acceleration in a dynamic programming network. In pruning, nodes are adaptively removed from the dynamic programming network when they are determined to not lie on an optimal path. We introduce an ε-pruning condition that extends pruning to include a possible error in the pruning step. This results in a greater reduction of the computation ...

Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are more accurate, but much slower, because they have to generate an overly specific concept description first. We have experimented with a variety of pruning methods, including two new methods that try to combine and integ...

Axon pruning and synapse elimination promote neural connectivity and synaptic plasticity. Stereotyped pruning of axons that originate in the hippocampal dentate gyrus (DG) and extend along the infrapyramidal tract (IPT) occurs during postnatal murine development by neurite retraction and resembles axon repulsion. The chemorepellent Sema3F is required for IPT axon pruning, dendritic spine remode...

Overtting is a widely observed pathology of induction algorithms. For induction algorithms that build decision trees, pruning is a common approach to correct overtting. Most common pruning techniques , do not account for one potentially important factor | multiple comparisons. Multiple comparisons occur whenever an induction algorithm examines several candidate models and selects the one that b...

In this paper we introduce a well-motivated abstract pruning criterion for LVCSR decoders based on the anticipated recombination of HMM state alignment paths. We show that several heuristical pruning methods common in dynamic network decoders are approximations of this pruning criterion. The abstract criterion is too complex to be applied directly in an efficient manner, so we derive approximat...

Developmental axon pruning is essential for normal brain wiring in vertebrates and invertebrates. How axon pruning occurs in vivo is not well understood. In a mosaic loss-of-function screen, we found that Bsk, the Drosophila JNK, is required for axon pruning of mushroom body γ neurons, but not their dendrites. By combining in vivo genetics, biochemistry, and high-resolution microscopy, we demon...