On Correcting Inputs: Inverse Optimization for Online Structured Prediction

Algorithm designers typically assume that the input data is correct, and then proceed to find "optimal" or "sub-optimal" solutions using this input data. However this assumption of correct data does not always hold in practice, especially in the context of online learning systems where the objective is to learn appropriate feature weights given some training samples. Such scenarios necessitate the study of inverse optimization problems where one is given an input instance as well as a desired output and the task is to adjust the input data so that the given output is indeed optimal. Motivated by learning structured prediction models, in this paper we consider inverse optimization with a margin, i.e., we require the given output to be better than all other feasible outputs by a desired margin. We consider such inverse optimization problems for maximum weight matroid basis, matroid intersection, perfect matchings, minimum cost maximum flows, and shortest paths and derive the first known results for such problems with a non-zero margin. The effectiveness of these algorithmic approaches to online learning for structured prediction is also discussed.Comment: Conference version to appear in FSTTCS, 201

Resource-Constrained Adaptive Search and Tracking for Sparse Dynamic Targets

This paper considers the problem of resource-constrained and noise-limited localization and estimation of dynamic targets that are sparsely distributed over a large area. We generalize an existing framework [Bashan et al, 2008] for adaptive allocation of sensing resources to the dynamic case, accounting for time-varying target behavior such as transitions to neighboring cells and varying amplitudes over a potentially long time horizon. The proposed adaptive sensing policy is driven by minimization of a modified version of the previously introduced ARAP objective function, which is a surrogate function for mean squared error within locations containing targets. We provide theoretical upper bounds on the performance of adaptive sensing policies by analyzing solutions with oracle knowledge of target locations, gaining insight into the effect of target motion and amplitude variation as well as sparsity. Exact minimization of the multi-stage objective function is infeasible, but myopic optimization yields a closed-form solution. We propose a simple non-myopic extension, the Dynamic Adaptive Resource Allocation Policy (D-ARAP), that allocates a fraction of resources for exploring all locations rather than solely exploiting the current belief state. Our numerical studies indicate that D-ARAP has the following advantages: (a) it is more robust than the myopic policy to noise, missing data, and model mismatch; (b) it performs comparably to well-known approximate dynamic programming solutions but at significantly lower computational complexity; and (c) it improves greatly upon non-adaptive uniform resource allocation in terms of estimation error and probability of detection.Comment: 49 pages, 1 table, 11 figure

How Much Change? The Saudi Arabia of Crown Prince Muhammad bin Salman

Since his father came to power in 2015, Muhammad bin Salman has been the driving force in Saudi Arabian politics. How much has the Crown Prince changed Saudi Arabia, and how much can he change it? F. Gregory Gause OOO is the John H. Lindsey \u2744 Chair, Professor of International Affairs and Head of the International Affairs Department at the Bush School of Government and Public Service, Texas A&M University.https://egrove.olemiss.edu/croft_spe/1004/thumbnail.jp

A teleoperated unmanned rotorcraft flight test technique

NASA and the U.S. Army are jointly developing a teleoperated unmanned rotorcraft research platform at the National Aeronautics and Space Administration (NASA) Langley Research Center. This effort is intended to provide the rotorcraft research community an intermediate step between wind tunnel rotorcraft studies and full scale flight testing. The research vehicle is scaled such that it can be operated in the NASA Langley 14- by 22-Foot Subsonic Tunnel or be flown freely at an outside test range. This paper briefly describes the system's requirements and the techniques used to marry the various technologies present in the system to meet these requirements. The paper also discusses the status of the development effort

Afterword: Vincentian Higher Education and Poverty Reduction

Former superior general G. Gregory Gay writes, “Education becomes a living witness to the Gospel of Christ when it is done by a community of people who are committed to the values of a Christian life and when it is directed towards a fuller and deeper communion of all members of the community.” Vincentian institutions must aid persons who are poor in every way they can. Their work toward poverty reduction includes “formation of the heart.” This means educating students so they empathize with poor persons, while trying to eliminate the causes of poverty and generally helping the poor lead lives of dignity. It is important to acknowledge the personhood of individuals who are poor and to work with them in alleviating their poverty. The Vincentian Family and their educational institutions must collaborate among themselves and with others to free the poor from the oppressive systems and circumstances under which they live

OM-VPE grown materials for high efficiency solar cells

Organometallic sources are available for all the III-V elements and a variety of dopants; thus it is possible to use the technique to grow a wide variety of semiconductor compounds. AlGaAsSb and AlGaInAs alloys for multijunction monolithic solar cells were grown by OM-VPE. While the effort concentrated on terrestrial applications, the success of OM-VPE grown GaAs/AlGaAs concentrator solar cells (23% at 400 suns) demonstrates that OM-VPE is suitable for growing high efficiency solar cells in large quantities for space applications. In addition, OM-VPE offers the potential for substantial cost reduction of photovoltaic devices with scale up and automation and due to high process yield from reproducible, uniform epitaxial growths with excellent surface morphology