Deep neural networks (DNNs) have been widely applied to active learning. Despite of its effectiveness, the generalization ability discriminative classifier (the softmax classifier) is questionable when there a significant distribution bias between labeled set and unlabeled set. In this paper, we attempt replace in deep network with nearest neighbor classifier, considering progressive within unk...

Trees have long been used as a graphical representation of species relationships. However complex evolutionary events, such as genetic reassortments or hybrid speciations which occur commonly in viruses, bacteria and plants, do not fit into this elementary framework. Alternatively, various network representations have been developed. Circular networks are a natural generalization of leaf-labele...

This paper is concerned with probability density estimation in high-dimensional settings. Simplified geometric arguments and supporting examples point to a performance bound which limits algorithm performance to that of either (1) nearest-neighbor or (2) single-kernel PDF estimators. A method of monitoring PDF estimation performance as well as recommendations for neural net and classification a...

Long-branch attraction is a well-known source of systematic error that can mislead phylogenetic methods; it is frequently invoked post hoc, upon recovering a different tree from the one expected based on prior evidence. We demonstrate that methods that do not force the data onto a single tree, such as spectral analysis, Neighbor-Net, and consensus networks, can be used to detect conflicting sig...

The study of particles containing heavy quarks is currently a major topic in High Energy Physics. In this paper. neural net trigger algorithms are developed to distinguish heavy quark (signal) events from light quark (background) events in a fixed target experiment. The event tracks which are parametrized by the impact parameter D and the angle @ of the track with respect to the beam line, vary...

We present DCOOL-NET, a scalable distributed in-network algorithm for sensor network localization based on noisy range measurements. DCOOL-NET operates by parallel, collaborative message passing between single-hop neighbor sensors, and involves simple computations at each node. It stems from an application of the majorization-minimization (MM) framework to the nonconvex optimization problem at ...

This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds. The SO-Net models the spatial distribution of point cloud by building a Self-Organizing Map (SOM). Based on the SOM, SO-Net performs hierarchical feature extraction on individual points and SOM nodes, and ultimately represents the input point cloud by a single feature vector. The rece...