In large-scale optimization problems, distributed asynchronous stochastic gradient descent (DASGD) is a commonly used algorithm. In most applications, there are often a large number of computing nodes asynchronously computing gradient information. As such, the gradient information received at a given iteration is often stale. In the presence of such delays, which can be unbounded, the convergence of DASGD is uncertain. The contribution of this paper is twofold. First, we propose a delay-adaptive variant of DASGD where we adjust each iteration's step-size based on the size of the delay, and prove asymptotic convergence of the algorithm on variationally coherent stochastic problems, a class of functions which properly includes convex, quasi-convex and star-convex functions. Second, we extend the convergence results of standard DASGD, used usually for problems with bounded domains, to problems with unbounded domains. In this way, we extend the frontier of theoretical guarantees for distributed asynchronous optimization, and provide new insights for practitioners working on large-scale optimization problems.

With the rapid growth of e-tail, the cost to handle returned online orders also increases significantly and has become a major challenge in the e-commerce industry. Accurate prediction of product returns allows e-tailers to prevent problematic transactions in advance. However, the limited existing work for modeling customer online shopping behaviors and predicting their return actions fail to integrate the rich information in the product purchase and return history (e.g., return history, purchase-no-return behavior, and customer/product similarity). Furthermore, the large-scale data sets involved in this problem, typically consisting of millions of customers and tens of thousands of products, also render existing methods inefficient and ineffective at predicting the product returns. To address these problems, in this paper, we propose to use a weighted hybrid graph to represent the rich information in the product purchase and return history, in order to predict product returns. The proposed graph consists of both customer nodes and product nodes, undirected edges reflecting customer return history and customer/product similarity based on their attributes, as well as directed edges discriminating purchase-no-return and no-purchase actions. Based on this representation, we study a random-walk-based local algorithm for predicting product return propensity for each customer, whose computational complexity depends only on the size of the output cluster rather than the entire graph. Such a property makes the proposed local algorithm particularly suitable for processing the large-scale data sets to predict product returns. To test the performance of the proposed techniques, we evaluate the graph model and algorithm on multiple e-commerce data sets, showing improved performance over state-of-the-art methods.

This paper describes the Dubuque Electricity Portal, a city-scale system aimed at supporting voluntary reductions of electricity consumption. The Portal provided each household with fine-grained feedback on its electricity use, as well as using incentives, comparisons, and goal setting to encourage conservation. Logs, a survey and interviews were used to evaluate the user experience of the Portal during a 20-week pilot with 765 volunteer households. Although the volunteers had already made a wide range of changes to conserve electricity prior to the pilot, those who used the Portal decreased their electricity use by about 3.7%. They also reported increased understanding of their usage, and reported taking an array of actions - both changing their behavior and their electricity infrastructure. The paper discusses the experience of the system's users, and describes challenges for the design of ECF systems, including balancing accessibility and security, a preference for time-based visualizations, and the advisability of multiple modes of feedback, incentives and information presentation.

We address theoptimal sensor arrangement problem, which is the determination of a geometric configuration of sensors such that the mean-squared error (MSE) in the estimation of an unknown trigonometric polynomial is minimum. Unsurprisingly, an arrangement in which sensors are spaced uniformly in each dimension is optimal. However, for multidimensional problems the minimum MSE is achieved with a much larger class of configurations that we callgeneralized regular arrangements. These arrangements are not necessarily generated by lattices and may exhibit great nonuniformity locally.

This paper addresses the problem of coverage control of a network of mobile sensors. In the current literature, this is commonly formulated as a locational optimization problem under the assumption that sensing performance is independent of the locations of sensors. We extend this work to a more general framework where the sensor model is location-dependent. We propose a distributed control law and coordination algorithm. If the global sensing performance function is known a priori, we prove that the algorithm is guaranteed to converge. To validate this algorithm, we conduct experiments with indoor and outdoor deployments of Cyclops cameras and model its sensing performance. This model is used to simulate deployments on 1D pathways and study the coverage obtained. We also examine the coverage in the case when the global sensing function is not known and is estimated in an online fashion.