motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the. SWISS-PROT 22 database for other sequences. that are members of the given

We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. For example, given a taxonomy

constrained least square fitting problem, bearing computational complexity of O(M + K2). Hence even with very large codebooks, our system can still process multiple frames per second. This efficiency significantly adds to the practical values of LLC for real applications.

with chromatic number 2 were first investigated systematically by M i 11 e r (who used the term property B) in the case of infinite edges. There now is a large literature of this subject both for finite and infinite sets. The main idea behind our investigations is that being simple or being a clique imposes

reverse scoring), and finally, the School Administration Autonomy Support Scale (e.g., "The school administration generally consults students before introducing new school policies'') had Cronbach alphas of .54, .56, and .65, respectively. 2 Participants rated items on a 7-point Likert

(CVs) (n ‫؍‬ 10) of 2-22% (78% of assays had CV <10%). A number of peptides gave CVs in the range 2-7% in five experiments (10 replicate runs each) continuously measuring 137 MRMs, demonstrating the precision achievable in complex digests. Depletion of six high abundance proteins

with diminishing returns to scale. The adjusted R 2 in model (3) is 13.6%. Because fund sequence number appears to have little effect on fund performance in our dataset, and because it is frequently unavailable in the Venture Economics database, we replace it with a dummy equaling one for first-time funds. We also

, this paper studies the problem of domain adaptation for sentiment classifiers, hereby a system is trained on labeled reviews from one source domain but is meant to be deployed on another. We propose a deep learning approach which learns to extract a meaningful representation for each review

, in uenced by their speci c experience. There is another layer that is folded in production/consumption, experience, power, and culture. This is technology. By technology I mean 'the use of scienti c knowledge to specify ways of doing things in a reproducible manner'. Technology is embodied

There has been recent progress on the problem of recognizing specific objects in very large datasets. The most common approach has been based on the bag-of-words (BOW) method, in which local image features are clustered into visual words. This can provide significant savings in memory compared