Daylight savings: what an answer to the perceptual variation problem cannot be

Significant variations in the way objects appear across different viewing conditions pose a challenge to the view that they have some true, determinate color. This view would seem to require that we break the symmetry between multiple appearances in favor of a single variant. A wide range of philosophical and non-philosophical writers have held that the symmetry can be broken by appealing to daylight viewing conditions—that the appearances of objects in daylight have a stronger, and perhaps unique, claim to reveal their true colors. In this note we argue that, whatever else its merits, this appeal to daylight is not a satisfactory answer to the problem posed by perceptual variation

Armchair Killers

Streaming video requires RealPlayer to view.The University Archives has determined that this item is of continuing value to OSU's history.What difference does it make if a political leader has experienced war himself? Does military experience among wartime leaders make them more or less bellicose? Should voters care whether or not their political leaders have had military experience?Ohio State University. Mershon Center for International Security StudiesEvent webpage, streaming video, and phot

COMBINED ROBUST OPTIMAL DESIGN, PATH AND MOTION PLANNING FOR UNMANNED AERIAL VEHICLE SYSTEMS SUBJECT TO UNCERTAINTY

Unmanned system performance depends heavily on both how the system is planned to be operated and the design of the unmanned system, both of which can be heavily impacted by uncertainty. This dissertation presents methods for simultaneously optimizing both of these aspects of an unmanned system when subject to uncertainty. This simultaneous optimization under uncertainty of unmanned system design and planning is demonstrated in the context of optimizing the design and flight path of an unmanned aerial vehicle (UAV) subject to an unknown set of wind conditions. This dissertation explores optimizing the path of the UAV down to the level of determining flight trajectories accounting for the UAVs dynamics (motion planning) while simultaneously optimizing design. Uncertainty is considered from the robust (no probability distribution known) standpoint, with the capability to account for a general set of uncertain parameters that affects the UAVs performance. New methods are investigated for solving motion planning problems for UAVs, which are applied to the problem of mitigating the risk posed by UAVs flying over inhabited areas. A new approach to solving robust optimization problems is developed, which uses a combination of random sampling and worst case analysis. The new robust optimization approach is shown to efficiently solve robust optimization problems, even when existing robust optimization methods would fail. A new approach for robust optimal motion planning that considers a “black-box” uncertainty model is developed based off the new robust optimization approach. The new robust motion planning approach is shown to perform better under uncertainty than methods which do not use a “black-box” uncertainty model. A new method is developed for solving design and path planning optimization problems for unmanned systems with discrete (graph-based) path representations, which is then extended to work on motion planning problems. This design and motion planning approach is used within the new robust optimization approach to solve a robust design and motion planning optimization problem for a UAV. Results are presented comparing these methods against a design study using a DOE, which show that the proposed methods can be less computationally expensive than existing methods for design and motion planning problems

Naval Postgraduate School Library

The article of record as published may be found at https://www.jstor.org/stable/20050097A review of the Naval Postgraduate School library's resources for research, assessed as represented by the library's 2001 website

Making Do with Less, or Coping with Upton\u27s Ghost

Each April the Strategic Studies Institute hosts a conference that addresses key strategic issues facing the Armed Forces and the Nation. This year\u27s theme, Strategy During the Lean Years: Learning from the Past and the Present, brought together scholars, serving and retired military officers, and civilian defense officials from the United States, Canada, and the United Kingdom to discuss strategy formulation in times of penury from Tacitus to Force XXI. Professor Eliot A. Cohen of Johns Hopkins University urges the Army to draw on lessons from its own history. More than one generation of American military professionals have inherited and perpetuated Civil War Major General Emory Upton s distrust of and disdain for civilians in general and politically elected or appointed civilian leaders in particular. As Professor Cohen indicates, the uncertainties of downsizing and reorganization coincide with the need to accommodate new technologies that could help the Army cope with the diverse threats that are part of what is still a very dangerous world. He cautions that in coping with this enormous challenge, the Army must be careful not to engage in the kind of introspection that may foster an institutionalized isolation from the nation it is sworn to defend. Professor Cohen suggests there are ways to keep America s Army truly the Army of the nation and its people. The way soldiers and leaders are recruited, trained, educated, and promoted must, he asserts, change to bring more and not less civilian influence into the Army. Professor Cohen urges the Army to go forward into Force XXI and to do so with both enhanced technologies and with an enhanced understanding of who and what it serves: the American people and the defense of their Constitution.https://press.armywarcollege.edu/monographs/1883/thumbnail.jp

RISK-BASED MULTIOBJECTIVE PATH PLANNING AND DESIGN OPTIMIZATION FOR UNMANNED AERIAL VEHICLES

Safe operation of unmanned aerial vehicles (UAVs) over populated areas requires reducing the risk posed by a UAV if it crashed during its operation. We considered several types of UAV risk-based path planning problems and developed techniques for estimating the risk to third parties on the ground. The path planning problem requires making trade-offs between risk and flight time. Four optimization approaches for solving the problem were tested; a network-based approach that used a greedy algorithm to improve the original solution generated the best solutions with the least computational effort. Additionally, an approach for solving a combined design and path planning problems was developed and tested. This approach was extended to solve robust risk-based path planning problem in which uncertainty about wind conditions would affect the risk posed by a UAV

Citizen science as a new tool in dog cognition research

The work of Á.M. was supported by the Hungarian Academy of Sciences (MTA 01 031).Family dogs and dog owners offer a potentially powerful way to conduct citizen science to answer questions about animal behavior that are difficult to answer with more conventional approaches. Here we evaluate the quality of the first data on dog cognition collected by citizen scientists using the Dognition. com website. We conducted analyses to understand if data generated by over 500 citizen scientists replicates internally and in comparison to previously published findings. Half of participants participated for free while the other half paid for access. The website provided each participant a temperament questionnaire and instructions on how to conduct a series of ten cognitive tests. Participation required internet access, a dog and some common household items. Participants could record their responses on any PC, tablet or smartphone from anywhere in the world and data were retained on servers. Results from citizen scientists and their dogs replicated a number of previously described phenomena from conventional lab-based research. There was little evidence that citizen scientists manipulated their results. To illustrate the potential uses of relatively large samples of citizen science data, we then used factor analysis to examine individual differences across the cognitive tasks. The data were best explained by multiple factors in support of the hypothesis that nonhumans, including dogs, can evolve multiple cognitive domains that vary independently. This analysis suggests that in the future, citizen scientists will generate useful datasets that test hypotheses and answer questions as a complement to conventional laboratory techniques used to study dog psychology.Publisher PDFPeer reviewe