Left-sided displacement of the abomasum (LDA) is a common disease in many dairy cattle breeds. A genome-wide screen for QTL for LDA in German Holstein (GH) cows indicated motilin (MLN) as a candidate gene on bovine chromosome 23. Genomic DNA sequence analysis of MLN revealed a total of 32 polymorphisms. All informative polymorphisms used for association analyses in a random sample of 1,136 GH c...

OBJECTIVE To determine whether low molecular weight heparin (LMWH) plus low-dose aspirin (LDA) is comparable in efficacy and safety to unfractionated heparin (UFH) plus LDA in the management of pregnant women with a history of recurrent spontaneous abortion secondary to antiphospholipid syndrome (APS). METHODS In a randomized prospective study, 60 women with a history of 3 or more consecutive...

The method based on local features has an advantage that the important local motion feature is represented as bag-of-features, but lacks the location information. Additionally, in order to employ an approach based on bag-of-features, language models represented by pLSA and LDA (Latent Dirichlet Allocation) have to be applied to. These are unsupervised learning, but they require the number of la...

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...