Abstract: In our paper we deal with the problem of low-level motion modelling and unusual event detection in urban surveillance videos. We model the direction of optical flow vectors at image pixels. We implemented and tested probability based approaches such as probability estimation, Mixture

We propose a novel approach to background cor-rection for Infinium HumanMethylation data to account for technical variation in background fluor-escence signal. Our approach capitalizes on a new use for the Infinium I design bead types to measure non-specific fluorescence in the colour channel opposite of their design (Cy3/Cy5). This provides tens of thousands of features for measuring back-ground instead of the much smaller number of negative control probes on the platforms (n=32 for HumanMethylation27 and n=614 for Human-Methylation450, respectively). We compare the per-formance of our methods with existing approaches, using technical replicates of both mixture samples and biological samples, and demonstrate that within- and between-platform artefacts can be sub-stantially reduced, with concomitant improvement in sensitivity, by the proposed methods.

We show a learning-based method for low-level vision problems. We set-up a Markov network of patches of the image and the underlying scene. A factorization approximation allows us to easily learn the parameters of the Markov network from synthetic examples of image/scene pairs, and to e ciently

world economy roughly accorded with the idea of three distinct economic systems: a capitalist first world, a socialist second world, and a developing third world which aimed for a middle way between the first two. The third world was characterized not only by its low levels of per capita GDP

that includes Aegis, an exokernel, and ExOS, an untrusted application-level operating system. Aegis defines the low-level interface to machine resources. Applications can allocate and use machine resources, efficiently handle events, and participate in resource revocation. Measurements show that most primitive

, is evidence of the above. However, the map-reduce paradigm is too low-level and rigid, and leads to a great deal of custom user code that is hard to maintain, and reuse. We describe a new language called Pig Latin that we have designed to fit in a sweet spot between the declarative style of SQL, and the low-level

Preface The Noisy Channel Coding Theorem discovered by C. E. Shannon in 1948 offered communication engineers the possibility of reducing error rates on noisy channels to negligible levels without sacrificing data rates. The primary obstacle to the practical use of this theorem has been

, or fairing, to low-pass filtering. We describe a very simple surface signal low-pass filter algorithm that applies to surfaces of arbitrary topology. As opposed to other existing optimization-based fairing methods, which are computationally more expensive, this is a linear time and space complexity algorithm

high accuracy by calculating power estimates for designs only after layout or floorplanning are complete In addition to being available only late in the design process, such tools are often quite slow, which compounds the difficulty of running them for a large space of design possibilities.

This article presents a process modeling language consisting of high level UML-based diagrams and a low level process language. The high level diagrams facilitate process program development, while the low level process language models processes as process programs. Between them is a mapping