Asking the right question in the right way is an art (and a science). In a community question-answering setting, a good question is not just one that is found to be useful by other people: a question is good if it is also presented clearly and shows prior research.  Using a community question-answering site that allows voting over the questions, we show that there is a notion of question quality that goes beyond mere popularity. We present techniques using latent topic models to automatically predict the quality of questions based on their content. Our best system achieves a prediction accuracy of 72%, beating out strong baselines by a significant amount. We also examine the effect of  question quality on the dynamics of user behavior and the longevity of questions.

In this paper, we study the task of aggregating user-generated trajectories in a differentially private manner. We present a new algorithm for this problem and demonstrate its effectiveness and practicality through detailed experiments on real-world data. We also show that under simple and natural assumptions, our algorithm has provable utility guarantees.

The privacy loss distribution (PLD) provides a tight characterization of the privacy loss of a mechanism in the context of differential privacy (DP). Recent work [18–20, 24] has shown that PLD-based accounting allows for tighter (ε, δ)-DP guarantees for many popular mechanisms compared to other known methods. A key question in PLD-based accounting is how to approximate any (potentially continuous) PLD with a PLD over any specified discrete support. We present a novel approach to this problem. Our approach supports both pessimistic estimation, which overestimates the hockey-stick divergence (i.e., δ) for any value of ε, and optimistic estimation, which underestimates the hockey-stick divergence. Moreover, we show that our pessimistic estimate is the best possible among all pessimistic estimates. Experimental evaluation shows that our approach can work with much larger discretization intervals while keeping a similar error bound compared to previous approaches and yet give a better approximation than an existing method [24].

Microscopy is a technique for making very small things visible to the unaided eye. An instrument used to make the small things visible to the naked (unaided) eye is called a microscope. Scanning electron microscopy is discussed in light of its principles, advantages, and applications. Comparisons of this system are made with the light microscopic and transmission electron systems. A cross section of pertinent literature on the scanning electron microscope, its development and use, has been integrated into the initial sections to provide a reference base for this general field. A detailed literature view on the use of this system in the field of wood science has also been included.

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