Observation of theorized glass-to-liquid transitions between low-density amorphous (LDA) and high-density amorphous (HDA) water states had been stymied by rapid crystallization below the homogeneous water nucleation temperature (∼235 K at 0.1 MPa). We report optical and X-ray observations suggestive of glass-to-liquid transitions in these states. Crack healing, indicative of liquid, occurs when...

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology. In this paper, we propose a fast and accurate batch algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA algorithms require repeated scanning of the entire corpus and searching the complete topic s...

Linear Discriminant Analysis (LDA) and Multilinear Principal Component Analysis (MPCA) are leading subspace methods for achieving dimension reduction based on supervised learning. Both LDA and MPCA use class labels of data samples to calculate subspaces onto which these samples are projected. Furthermore, both methods have been successfully applied to face recognition. Although LDA and MPCA sha...

In this paper, Linear Discriminant Analysis (LDA) is investigated with respect to the combination of different acoustic features for automatic speech recognition. It is shown that the combination of acoustic features using LDA does not consistently lead to improvements in word error rate. A detailed analysis of the recognition results on the Verbmobil (VM II) and on the English portion of the E...

Latent Dirichlet Allocation (LDA) is an unsupervised, statistical approach to document modeling that discovers latent semantic topics in large collections of text documents. LDA posits that words carry strong semantic information, and documents discussing similar topics will use a similar group of words. Latent topics are thus discovered by identifying groups of words in the corpus that frequen...

Linear discriminant analysis (LDA) has played an important role for dimension reduction in patter recognition field. Basically, LDA has three deficiencies in dealing with classification problems. First, LDA is well-suited only for normally distributed data. Second, the number of features can be extracted are limited by the rank of between-class scatter matrix. Third, the singularity problem ari...

Linear Discriminant Analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled problems where the number of data samples is smaller than the dimension of data space, it is difficult to apply the LDA due to the singularity of scatter matrices caused by high dimensionality. In order to make the LDA ap...

BACKGROUND More studies based on gene expression data have been reported in great detail, however, one major challenge for the methodologists is the choice of classification methods. The main purpose of this research was to compare the performance of linear discriminant analysis (LDA) and its modification methods for the classification of cancer based on gene expression data. METHODS The clas...

Model based deep neural network (DNN) adaptation approaches often require multi-pass decoding in test time. Input feature based DNN adaptation, for example, based on latent Dirichlet allocation (LDA) clustering, provide a more efficient alternative. In conventional LDA clustering, the transition and correlation between neighboring clusters is ignored. In order to address this issue, a recurrent...