this paper, how can we recognize the identity of Figs. 2.1 and 2.2? Do we use' learning and knowledge to interpret what we see, or do we somehow automatically see the world as stable and independent bf lighting? What portions of scenes can we understand from local features alone, and what

of group property visual descriptors. These de-scriptors are scene-independent, and can be effectively ap-plied to public-scene with variety of crowd densities and distributions. Extensive experiments on hundreds of public scene video clips demonstrate that such property descrip-tors are not only useful

changes are principally driven by local supply/demand shocks that are independent of both credit-risk factors and standard proxies for liquidity. * Collin-Dufresne is at Carnegie Mellon University. Goldstein is at Washington University in St. Louis. Martin is at Arizona State University. A significant

, and furrows. During tracking, detailed parametric descriptions of the facial features are extracted. With these parameters as the inputs, a group of action units (neutral expression, six upper face AUs and 10 lower face AUs) are recognized whether they occur alone or in combinations. The system has achieved

-rectly on the data without obtaining locks. The algorithm must know some semantics of the operations. However the algorithm’s overall structure is independent of the semantic information, allowing the algorithm to be adapted to many situations. An example application of the algorithm to group text editing is given

of group signature schemes have the undesirable property that the length of the public key is linear in the size of the group. In this paper we propose the first group signature scheme whose public key and signatures have length independent of the number of group members and which can therefore also

broadcasts to the group as an entity, group membership changes, and even migration of an activity from one place to another appear to occur instantaneously -- in other words, synchronously. A major advantage to this approach is that many aspects of a distributed application can be treated independently

Riffled independence is a generalized notion of probabilistic independence that has been shown to be naturally applicable to ranked data. In the riffled independence model, one assigns rankings to two disjoint sets of items independently, then in a second stage, interleaves (or riffles) the two

Using DNA microarray technology, it is now possible to measure the expression levels of tens of thousands of genes. Statistical analysis of these expression levels provides insight into the function of genes and their biological pathways, as well as information about the genomic underpinnings of many common diseases. Cluster analysis is a form of unsupervised learning commonly used to analyze microarray data, and there are several different types of cluster analysis to choose from. It is widely acknowledged that the different types of cluster analysis can produce vastly inconsistent results, yet there is no known way to deal with these inconsistencies. In this thesis, I present a novel approach to the cluster analysis of microarray data. The proposed methodology combines and distills the information generated by different types of cluster analysis, and produces a representative clustering structure. Several new statistics are developed to identify dominant clusters and assess consistency across clustering algorithms. Using real data from leukemia patients, the proposed methodology is shown to outperform the naïve choice

journal of visual culture journal of visual culture