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Scaffolding molecules at the postsynaptic membrane form the foundation of excitatory synaptic transmission by establishing the architecture of the postsynaptic density (PSD), but the small size of the synapse has precluded measurement of PSD organization in live cells. We measured the internal structure of the PSD in live neurons at approximately 25 nm(More)
Two classifiers-Support Vector Machine (SVM) and Conditional Random Fields (CRFs) are applied here for the recognition of biomedical named entities. According to their different characteristics, the results of two classifiers are merged to achieve better performance. We propose an automatic corpus expansion method for SVM and CRF to overcome the shortage of(More)
This paper presents a new active learning paradigm which considers not only the uncertainty of the classifier but also the diversity of the corpus. The two measures for uncertainty and diversity were combined using the MMR (Maximal Marginal Relevance) method to give the sampling scores in our active learning strategy. We incorporated MMR-based active(More)
The registration of 3D form features is essential to the supporting of reverse shape design processes. Extracting an editable shape feature from unordered data points is notoriously hard. We present a new method to extract a shape instance plus design parameters from a ridge structure contained in a freeform surface, where the ridge has no predefined path,(More)
— Blind adaptive multiuser detection for direct sequence code division multiple access (DS-CDMA) signals over static and time-varying intersymbol interference (ISI) limited channels is considered. Blind adaptive detectors must be ro-bustified for ISI channels, when there is significant mismatch between the received signature vector and the transmitted code(More)
SUMMARY POSBIOTM-NER is a trainable biomedical named-entity recognition system. POSBIOTM-NER can be automatically trained and adapted to new datasets without performance degradation, using CRF (conditional random field) machine learning techniques and automatic linguistic feature analysis. Currently, we have trained our system on three different datasets.(More)