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Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
An optimal surface detection method capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations is developed. Expand
Cell population tracking and lineage construction with spatiotemporal context
This paper presents a fully automated multi-target tracking system that can efficiently cope with these challenges while simultaneously tracking and analyzing thousands of cells observed using time-lapse phase contrast microscopy. Expand
CollAFL: Path Sensitive Fuzzing
A coverage sensitive fuzzing solution that mitigates path collisions by providing more accurate coverage information, while still preserving low instrumentation overhead and armed with the three fuzzing strategies, CollAFL outperforms AFL in terms of both code coverage and vulnerability discovery. Expand
Temporal Subspace Clustering for Human Motion Segmentation
  • Sheng Li, K. Li, Y. Fu
  • Mathematics, Computer Science
  • IEEE International Conference on Computer Vision…
  • 7 December 2015
A temporal Laplacian regularization is designed, which encodes the sequential relationships in time series data and improves the clustering accuracy, compared to the state-of-the-art subspace clustering methods. Expand
A Deep Learning Approach to Link Prediction in Dynamic Networks
A novel deep learning framework, i.e., Conditional Temporal Restricted Boltzmann Machine (ctRBM), which predicts links based on individual transition variance as well as influence introduced by local neighbors is proposed, which outperforms existing algorithms in link inference on dynamic networks. Expand
Nonnegative Mixed-Norm Preconditioning for Microscopy Image Segmentation
This work proposes a general algebraic framework for preconditioning microscopy images and achieves unprecedented segmentation accuracy of 97.9% for CNS stem cells, and 93.4% for human red blood cells in challenging images. Expand
A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells
The BBO-M uses the structure of biogeography-based optimization algorithm (BBO), and both the mutation motivated from the differential evolution (DE) algorithm and the chaos theory are incorporated into the BBO structure for improving the global searching capability of the algorithm. Expand
Prediction of Human Activity by Discovering Temporal Sequence Patterns
  • K. Li, Y. Fu
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 August 2014
This work proposes a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability, and presents a predictive accumulative function (PAF) to depict the predictability of each kind of activity. Expand
Support vector machine classification for large data sets via minimum enclosing ball clustering
The approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers. Expand
A Novel Semi-Supervised Deep Learning Framework for Affective State Recognition on EEG Signals
This work comes up with a novel semi-supervised deep structured framework that is more adapted to the EEG classification problem and extends it to the active learning scenario, which solves the costly labeling problem. Expand