7,272 research outputs found
Precise Modelling of Compensating Business Transactions and its Application to BPEL
We describe the StAC language which can be used to specify the orchestration of activities in long running business transactions. Long running business transactions use compensation to cope with exceptions. StAC supports sequential and parallel behaviour as well as exception and compensation handling. We also show how the B notation may be combined with StAC to specify the data aspects of transactions. The combination of StAC and B provides a rich formal notation which allows for succinct and precise specification of business transactions. BPEL is an industry standard language for specifying business transactions and includes compensation constructs. We show how a substantial subset of BPEL can be mapped to StAC thus demonstrating the expressiveness of StAC and providing a formal semantics for BPEL
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Optimal investment with inside information and parameter uncertainty
An optimal investment problem is solved for an insider who has access to noisy information related to a future stock price, but who does not know the stock price drift. The drift is filtered from a combination of price observations and the privileged information, fusing a partial information scenario with enlargement of filtration techniques. We apply a variant of the Kalman-Bucy filter to infer a signal, given a combination of an observation process and some additional information. This converts the combined partial and inside information model to a full information model, and the associated investment problem for HARA utility is explicitly solved via duality methods. We consider the cases in which the agent has information on the terminal value of the Brownian motion driving the stock, and on the terminal stock price itself. Comparisons are drawn with the classical partial information case without insider knowledge. The parameter uncertainty results in stock price inside information being more valuable than Brownian information, and perfect knowledge of the future stock price leads to infinite additional utility. This is in contrast to the conventional case in which the stock drift is assumed known, in which perfect information of any kind leads to unbounded additional utility, since stock price information is then indistinguishable from Brownian information
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