Output Masks 
In this paper we propose a novel method for achieving average consensus in a multiagent network while avoiding to disclose the initial states of the individual agents. In order to achieve privacy protection of the state variables, we introduce maps, called output masks, which alter the value of the states before publicly broadcasting them. These output masks are local (i.e., implemented independently by each agent), deterministic, time-varying and converging asymptotically to the true state. The resulting masked system is also time-varying and has the original (unmasked) system as its limit system. It is shown in the paper that the masked system has the original average consensus value as a global attractor. However, in order to preserve privacy, it cannot share an equilibrium point with the unmasked system, meaning that in the masked system the global attractor cannot be also stable. …
Xu
The exponential growth of information on the Internet has created a big challenge for retrieval systems in terms of yielding relevant results. This challenge requires automatic approaches for reformatting or expanding users’ queries to increase recall. Query expansion (QE), a technique for broadening users’ queries by appending additional tokens or phrases bases on semantic similarity metrics, plays a crucial role in overcoming this challenge. However, such a procedure increases computational complexity and may lead to unwanted noise in information retrieval. This paper attempts to push the state of the art of QE by developing an automated technique using high dimensional clustering of word vectors to create effective expansions with reduced noise. We implemented a command line tool, named Xu, and evaluated its performance against a dataset of news articles, concluding that on average, expansions generated using this technique outperform those generated by previous approaches, and the base user query. …
Pareto-Smoothed Importance Sampling (PSIS)
While it’s always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation’. We propose two diagnostic algorithms to alleviate this problem. The Pareto-smoothed importance sampling (PSIS) diagnostic gives a goodness of fit measurement for joint distributions, while simultaneously improving the error in the estimate. The variational simulation-based calibration (VSBC) assesses the average performance of point estimates. …
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