Decoding ‘Maximum Entropy’ Deconvolution

For over five decades, the mathematical procedure termed “maximum entropy” (M-E) has been used to deconvolve structure in spectra, optical and otherwise, although quantitative measures of performance remain unknown. Here, we examine this procedure analytically for the lowest two orders for a Lorentzian feature, obtaining expressions for the amount of sharpening and identifying how spurious structures appear. Illustrative examples are provided. These results enhance the utility of this widely used deconvolution approach to spectral analysis

Optimizing in the Dark: Learning an Optimal Solution through a Simple Request Interface

Network resource reservation systems are being developed and deployed, driven by the demand and substantial benefits of providing performance predictability for modern distributed applications. However, existing systems suffer limitations: They either are inefficient in finding the optimal resource reservation, or cause private information (e.g., from the network infrastructure) to be exposed (e.g., to the user). In this paper, we design BoxOpt, a novel system that leverages efficient oracle construction techniques in optimization and learning theory to automatically, and swiftly learn the optimal resource reservations without exchanging any private information between the network and the user. We implement a prototype of BoxOpt and demonstrate its efficiency and efficacy via extensive experiments using real network topology and trace. Results show that (1) BoxOpt has a 100% correctness ratio, and (2) for 95% of requests, BoxOpt learns the optimal resource reservation within 13 seconds

A Dynamic Hierarchy of Intelligent Agents for Network Management

Routing as well as the management of communication networks that support hybrid types of communications requiring quality of service is a hard problem. We present here a framework1 that decomposes the network into a hierarchy of abstract views of the network that summarizes the available bandwidth resources and highlights bottlenecks in the network in order to reduce the complexity of the previously mentioned tasks. This framework can easily be distributed to a hierarchy of intelligent agents. This framework is technology independent and can be applied to any connection-oriented communication network