9,654 research outputs found
A Shannon Approach to Secure Multi-party Computations
In secure multi-party computations (SMC), parties wish to compute a function
on their private data without revealing more information about their data than
what the function reveals. In this paper, we investigate two Shannon-type
questions on this problem. We first consider the traditional one-shot model for
SMC which does not assume a probabilistic prior on the data. In this model,
private communication and randomness are the key enablers to secure computing,
and we investigate a notion of randomness cost and capacity. We then move to a
probabilistic model for the data, and propose a Shannon model for discrete
memoryless SMC. In this model, correlations among data are the key enablers for
secure computing, and we investigate a notion of dependency which permits the
secure computation of a function. While the models and questions are general,
this paper focuses on summation functions, and relies on polar code
constructions
Polar Codes for Distributed Hierarchical Source Coding
We show that polar codes can be used to achieve the rate-distortion functions
in the problem of hierarchical source coding also known as the successive
refinement problem. We also analyze the distributed version of this problem,
constructing a polar coding scheme that achieves the rate distortion functions
for successive refinement with side information.Comment: 14 page
Mark 3 interactive data analysis system
The interactive data analysis system, a major subset of the total Mark 3 very long baseline interferometry (VLBI) software system is described. The system consists of two major and a number of small programs. These programs provide for the scientific analysis of the observed values of delay and delay rate generated by the VLBI data reduction programs and product the geophysical and astrometric parameters which are among the ultimate products of VLBI. The two major programs are CALC and SOLVE. CALC generates the theoretical values of VLBI delay rate as well as partial derivatives based on apriori values of the geophysical and astronometric parameters. SOLVE is a least squares parameters estimation program which yields the geophysical and astrometric parameters using the observed values by the data processing system and theoretical values and partial derivatives provided by CALC. SOLVE is a highly interactive program in which the user selects the exact form of the recovered parameters and the data to be accepted into the solution
Urban Street Network Analysis in a Computational Notebook
Computational notebooks offer researchers, practitioners, students, and educators the ability to interactively conduct analytics and disseminate reproducible workflows that weave together code, visuals, and narratives. This article explores the potential of computational notebooks in urban analytics and planning, demonstrating their utility through a case study of OSMnx and its tutorials repository. OSMnx is a Python package for working with OpenStreetMap data and modeling, analyzing, and visualizing street networks anywhere in the world. Its official demos and tutorials are distributed as open-source Jupyter notebooks on GitHub. This article showcases this resource by documenting the repository and demonstrating OSMnx interactively through a synoptic tutorial adapted from the repository. It illustrates how to download urban data and model street networks for various study sites, compute network indicators, visualize street centrality, calculate routes, and work with other spatial data such as building footprints and points of interest. Computational notebooks help introduce methods to new users and help researchers reach broader audiences interested in learning from, adapting, and remixing their work. Due to their utility and versatility, the ongoing adoption of computational notebooks in urban planning, analytics, and related geocomputation disciplines should continue into the future
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