This paper introduces AnVILMEDFORD, an R package that aims to enable MEDFORD metadata annotation from within the cloud based AnVIL platform for computational analysis. AnVILMEDFORD helps solve the problem of documentation within the AnVIL environment by providing bidirectional tools that both allow a user to generate MEDFORD documentation from a workspace, and also allow a user to update an AnVIL workspace starting with a MEDFORD meta-data file. AnVILMEDFORD accomplishes this by leveraging the Bioconductor AnVIL package and the AnVIL API to download and process AnVIL metadata, Tree-sitter-medford module along with the AnVIL API to update the workspace in the cloud. An implementation is available at https://github.com/bjstubbs/AnVILMEDFORD

Sensor localization problem is one of the fundamental issues in Wireless Sensor Networks (WSN). In this paper, we investigate this problem and present an innovative Radio Interferometric Positioning System (RIPS) for WSN - Stochastic Radio Interferometric Positioning System with Unsynchronized Modulated Signals (SRIPS_UMS). To the best of our knowledge, we are the first to utilize unsynchronized modulated signals for radio interferometric positioning and demonstrate its viability in theory. Although previous radio interferometric positioning methods provide centimeter accuracy, they are still not widely adopted due to (1) strict hardware requirements for Received Signal Strength Indicator (RSSI) circuitry (2) time synchronization problem between distributed hardware. SRIPS_UMS overcomes these limitations by using unsynchronized modulated signals. Its viability and advantages are demonstrated through mathematical models and simulations. Extensive simulations based on radio chip CC2420 demonstrate that SRIPS_UMS is an efficient positioning system for outdoor WSN.

We aim to reduce the overhead of information gathering and updates in a scenario of semi-autonomous resource management agents in a multi-agent environment. We investigate the importance of coordinating timing of events compared with the level of information exchange among the agents; and how this affects deviations from best-case management as predicted by theoretical models. We demonstrate that asynchronous interaction between management agents requires only a small amount of information exchange between the agents to achieve near-optimal results. This suggests that there are lightweight alternatives to centralized data gathering and planning to achieve autonomic service management.

Performance problems in asynchronous massively parallel programs are often the result of unforeseen and complex asynchronous interactions between autonomous processing elements. Then performance problems are not inefficiencies in source code, but gaps in the algorithm designer's understanding of a complex physical system. The analyst forms hypotheses about the probable causes or possible improvements, and verifies these hypotheses by modifying the program and testing it again. These hypotheses can be formed by a variety of methods, from simple and mostly fruitful techniques for suggesting possible source code improvements to the difficult, indirect, and possibly futile activity of visualizing execution. The author describes a visualization system for massively parallel execution data and shows how drawbacks in other analysis methods sometimes make visualization necessary despite its difficulty.>

Based on experiences with various debugging tools, the authors believe that the ideal tool represents several forms of integration: integration of scope from simple debugging to advanced performance monitoring; integration of techniques such as graphical and textual displays and real-time and postmortem analysis; connections between event monitoring and execution control; coordination of instrumentation in the parallel operating system with that in the tool; and a considered user interface throughout all components of the tool. Each of these forms of integration is briefly discussed.>