Opportunities for aircraft controls research

Several problems which drive aircraft control technology are discussed. Highly unstable vehicles, flutter speed boundary expansion, and low level automated flight that follows terrain are discussed

Is Neoclassical Economics still Entrepreneurless?

We review and evaluate some recent contributions on the modeling of entrepreneurship within a neoclassical framework, analyzing how and to what extent the fundamental ingredients suggested in the social science literature were captured. We show how these approaches are important in stressing the main elements of a complex picture without being able to completely describe it. However, each modeling attempt focuses only on one specific feature of entrepreneurship. The entrepreneurial function broadly perceived eludes analytical tractability. As a consequence, the models can be useful in analyzing the effect of entrepreneurial behavior at an aggregate level, but not at explaining individual choices. From these observations we highlight how a simplistic interpretation of the existing mainstream approaches incorporating entrepreneurship runs the risk of leading to distortionary policy interventions.Entrepreneurial Choice; Entrepreneurship; Innovation; Neoclassical Modeling; Uncertainty

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Biometric Backdoors: A Poisoning Attack Against Unsupervised Template Updating

In this work, we investigate the concept of biometric backdoors: a template poisoning attack on biometric systems that allows adversaries to stealthily and effortlessly impersonate users in the long-term by exploiting the template update procedure. We show that such attacks can be carried out even by attackers with physical limitations (no digital access to the sensor) and zero knowledge of training data (they know neither decision boundaries nor user template). Based on the adversaries' own templates, they craft several intermediate samples that incrementally bridge the distance between their own template and the legitimate user's. As these adversarial samples are added to the template, the attacker is eventually accepted alongside the legitimate user. To avoid detection, we design the attack to minimize the number of rejected samples. We design our method to cope with the weak assumptions for the attacker and we evaluate the effectiveness of this approach on state-of-the-art face recognition pipelines based on deep neural networks. We find that in scenarios where the deep network is known, adversaries can successfully carry out the attack over 70% of cases with less than ten injection attempts. Even in black-box scenarios, we find that exploiting the transferability of adversarial samples from surrogate models can lead to successful attacks in around 15% of cases. Finally, we design a poisoning detection technique that leverages the consistent directionality of template updates in feature space to discriminate between legitimate and malicious updates. We evaluate such a countermeasure with a set of intra-user variability factors which may present the same directionality characteristics, obtaining equal error rates for the detection between 7-14% and leading to over 99% of attacks being detected after only two sample injections.Comment: 12 page

FEDERAL GRAZING REFORM AND AVOIDABLE RISK

Recent rangeland reform attempts have increased ranchers'Â’ uncertainty of retaining grazing permits on federal land. This uncertainty is analyzed with a model of grazing on federal land. Ranchers facing this uncertainty will behave differently than if they were guaranteed the renewal of grazing permits at constant real grazing fees. It is shown that the socially optimal outcome may be achieved by adding avoidable risk through targeted rangeland reform. Rangeland reform attempts that create unavoidable risk can make both ranchers and environmental groups worse off.Agricultural and Food Policy,