A Testbed for Developing and Evaluating GNSS Signal Authentication Techniques

An experimental testbed has been created for developing and evaluating Global Navigation Satellite System (GNSS) signal authentication techniques. The testbed advances the state of the art in GNSS signal authentication by subjecting candidate techniques to the strongest publicly-acknowledged GNSS spoofing attacks. The testbed consists of a real-time phase-coherent GNSS signal simulator that acts as spoofer, a real-time softwaredefined GNSS receiver that plays the role of defender, and post-processing versions of both the spoofer and defender. Two recently-proposed authentication techniques are analytically and experimentally evaluated: (1) a defense based on anomalous received power in a GNSS band, and (2) a cryptographic defense against estimation-and-replay-type spoofing attacks. The evaluation reveals weaknesses in both techniques; nonetheless, both significantly complicate a successful GNSS spoofing attackAerospace Engineering and Engineering Mechanic

Three great american disinflations

In this paper, we examine three famous episodes of deliberate deflation (or disinflation) in U.S. history, including episodes following the Civil War, World War I, and the Volcker disinflation of the early 1980s. These episodes were associated with widely divergent effects on the real economy, which we attribute both to differences in the policy actions undertaken, and to the transparency and credibility of the monetary authorities. We attempt to account for the salient features of each episode within the context of a stylized DSGE model. Our model simulations indicate how a more predictable policy of gradual deflation could have helped avoid the sharp post-WWI depression. But our analysis also suggests that the strong argument for gradualism under a transparent monetary regime becomes less persuasive if the monetary authority lacks credibility; in this case, an aggressive policy stance (as under Volcker) can play a useful signalling role by making a policy shift more apparent to private agents. JEL Classification: E31, E32, E5

Modeling the mobility of living organisms in heterogeneous landscapes: Does memory improve foraging success?

Thanks to recent technological advances, it is now possible to track with an unprecedented precision and for long periods of time the movement patterns of many living organisms in their habitat. The increasing amount of data available on single trajectories offers the possibility of understanding how animals move and of testing basic movement models. Random walks have long represented the main description for micro-organisms and have also been useful to understand the foraging behaviour of large animals. Nevertheless, most vertebrates, in particular humans and other primates, rely on sophisticated cognitive tools such as spatial maps, episodic memory and travel cost discounting. These properties call for other modeling approaches of mobility patterns. We propose a foraging framework where a learning mobile agent uses a combination of memory-based and random steps. We investigate how advantageous it is to use memory for exploiting resources in heterogeneous and changing environments. An adequate balance of determinism and random exploration is found to maximize the foraging efficiency and to generate trajectories with an intricate spatio-temporal order. Based on this approach, we propose some tools for analysing the non-random nature of mobility patterns in general.Comment: 14 pages, 4 figures, improved discussio

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

We investigate whether a robot arm can learn to pick and throw arbitrary objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring reliable pre-throw conditions (e.g. initial pose of object in manipulator) to handling varying object-centric properties (e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e., using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot, is able to grasp and throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 85% throwing accuracy); and generalizes to new objects and target locations. Videos are available at https://tossingbot.cs.princeton.eduComment: Summary Video: https://youtu.be/f5Zn2Up2RjQ Project webpage: https://tossingbot.cs.princeton.ed

The Knowledge Gap in Workplace Retirement Investing and the Role of Professional Advisors

The dramatic shift from traditional pension plans to participant-directed 401(k) plans has increased the obligation of individual investors to take responsibility for their own retirement planning. With this shift comes increasing evidence that investors are making poor investment decisions. This Article seeks to uncover the reasons for poor investment decisions. We use a simulated retirement investing task and a new financial literacy index to evaluate the role of financial literacy in retirement investment decisionmaking in a group of nonexpert participants. Our results suggest that individual employees often lack the skills necessary to support the current model of participant-directed investing. We show that less knowledgeable participants allocate too little money to equity, engage in naive diversification, fail to identify dominated funds, and are inattentive to fees. Over the duration of a retirement account, these mistakes can cost investors hundreds of thousands of dollars. We then explore the capacity of professional advisors to mitigate this problem. Using the same study with a group of professional advisors, we document a predictable but nonetheless dramatic knowledge gap between professionals and ordinary investors. The professional advisors were far more financially literate and made better choices among investment alternatives. Our results highlight the potential value of professional advice in mitigating the effects of financial illiteracy in retirement planning. Our findings suggest that, in weighing the costs of heightened regulation against the value of reducing possible conflicts of interest, regulators need to be sensitive to the knowledge gap