Optimum cycle ratio (OCR) algorithms are fundamental to the performance analysis of (digital or manufacturing) systems with cycles. Some applications in the computer-aided design field include cycle time and slack optimization for circuits, retiming, timing separation analysis, and rate analysis. There are many OCR algorithms, and since a superior time complexity in theory does not mean a superior time complexity in practice, or vice-versa, it is important to know how these algorithms perform in practice on real circuit benchmarks. A recent published study experimentally evaluated almost all the known OCR algorithms, and determined the fastest one among them. This article improves on that study in the following ways: (1) it focuses on the fastest OCR algorithms only; (2) it provides a unified theoretical framework and a few new results; (3) it runs these algorithms on the largest circuit benchmarks available; (4) it compares the algorithms in terms of many properties in addition to running times such as operation counts, convergence behavior, space requirements, generality, simplicity, and robustness; (5) it analyzes the experimental results using statistical techniques and provides asymptotic time complexity of each algorithm in practice; and (6) it provides clear guidance to the use and implementation of these algorithms together with our algorithmic improvements.

In targeted display advertising, the goal is to identify the best opportunities to display a banner ad to an online user who is most likely to take a desired action such as purchasing a product or signing up for a newsletter. Finding the best ad impression, i.e., the opportunity to show an ad to a user, requires the ability to estimate the probability that the user who sees the ad on his or her browser will take an action, i.e., the user will convert. However, conversion probability estimation is a challenging task since there is extreme data sparsity across different data dimensions and the conversion event occurs rarely. In this paper, we present our approach to conversion rate estimation which relies on utilizing past performance observations along user, publisher and advertiser data hierarchies. More specifically, we model the conversion event at different select hierarchical levels with separate binomial distributions and estimate the distribution parameters individually. Then we demonstrate how we can combine these individual estimators using logistic regression to accurately identify conversion events. In our presentation, we also discuss main practical considerations such as data imbalance, missing data, and output probability calibration, which render this estimation problem more difficult but yet need solving for a real-world implementation of the approach. We provide results from real advertising campaigns to demonstrate the effectiveness of our proposed approach.

Web graphs are approximate snapshots of the web, created by search engines. They are essential to monitor the evolution of the web and to compute global properties like PageRank values of web pages. Their continuous monitoring requires a notion of graph similarity to help measure the amount and significance of changes in the evolving web. As a result, these measurements provide means to validate how well search engines acquire content from the web. In this paper we propose five similarity schemes: three of them we adapted from existing graph similarity measures, and two we adapted from well-known document and vector similarity methods (namely, the shingling method and random projection based method). We empirically evaluate and compare all five schemes using a sequence of web graphs from Yahoo!, and study if the schemes can identify anomalies that may occur due to hardware or other problems.

Maximum and minimum mean cycle problems are important problems with many applications in performance analysis of synchronous and asynchronous digital systems including rate analysis of embedded systems, in discrete-event systems, and in graph theory. Karp's algorithm is one of the fastest and commonest algorithms for both of these problems. We present this paper mainly in the context of the maximum mean cycle problem. We show that Karp's algorithm processes more vertices and arcs than needed to nd the maximum cycle mean of a digraph. This observation motivated us to propose a new graph unfolding scheme that remedies this de ciency and leads to three faster algorithms with di erent c haracteristics. Asymptotic analysis tells us that our algorithms always run faster than Karp's algorithm. Experiments on benchmark graphs con rm this fact for most of the graphs. Like Karp's algorithm, they are also applicable to both the maximum and minimum mean cycle problems. Moreover, one of them is among the fastest to date.

Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for advertisers to submit a bid for each selected RTB ad request in milliseconds. Restricted by the budget, the goal is to buy a set of ad impressions to reach as many targeted users as possible. A desired action (conversion), advertiser specific, includes purchasing a product, filling out a form, signing up for emails, etc. In addition, advertisers typically prefer to spend their budget smoothly over the time in order to reach a wider range of audience accessible throughout a day and have a sustainable impact. However, since the conversions occur rarely and the occurrence feedback is normally delayed, it is very challenging to achieve both budget and performance goals at the same time. In this paper, we present an online approach to the smooth budget delivery while optimizing for the conversion performance. Our algorithm tries to select high quality impressions and adjust the bid price based on the prior performance distribution in an adaptive manner by distributing the budget optimally across time. Our experimental results from real advertising campaigns demonstrate the effectiveness of our proposed approach.