Paintings and their implicit presuppositions : a preliminary report

In a series of earlier papers (Social Science Working Papers 350, 355. 357) we have studied the ways in which differences in "implicit presupposi tions" (i. e •• differences in world views) cause scientists and historians to reach differing conclusions from a consideration of the same evidence. In this paper we show that paintings are characterized by implicit presuppositions similar to those that characterize the written materials -- essays, letters, scientific papers -- we have already studied

Paintings and their implicit presuppositions: High Renaissance and Mannerism

All art historians who are interested in questions of "styles" or "schools" agree in identifying a High Renaissance school of Italian painting. There is, however, a disagreement, which has seemed nonterminating, regarding Mannerism: Is it another distinct school or is it merely a late development of the Renaissance school? We believe that this disagreement can be terminated by distinguishing questions of fact about paintings from questions about the definitions of schools. To this end we have had two representative subsets of paintings--one earlier, one later--rated on four of the dimensions of implicit presuppositions that we have introduced in other Working Papers. When the paintings are scaled in this way a very distinct profile emerges for the earlier, or Renaissance, paintings. In contrast, the later, or Mannerist, paintings are so heterogeneous that we conclude that they are best described as deviations from the Renaissance profile, rather than a separate school. These results are not unimportant--at least for art historians. But they are more important methodologically inasmuch as the procedures applied here can be used in classifying and distinguishing from one another all kind of cultural products

Challenges encountered during acid resin transfer preparation of fossil fish from Monte Bolca, Italy

Copyright: Palaeontological Association May 2015. This is an open access article, available to all readers online, published under a creative commons licensing (https://creativecommons.org/licenses/by/4.0/). The file attached is the published version of the article

Angular Power Spectra of the COBE DIRBE Maps

The angular power spectra of the infrared maps obtained by the DIRBE (Diffuse InfraRed Background Experiment) instrument on the COBE satellite have been obtained by two methods: the Hauser-Peebles method previously applied to the DMR maps, and by Fourier transforming portions of the all-sky maps projected onto a plane. The two methods give consistent results, and the power spectrum of the high-latitude dust emission is C_\ell \propto \ell^{-3} in the range 2 < \ell < 300.Comment: ApJ in press. 15 pages with 5 included figure

Data Analytics in Higher Education: Key Concerns and Open Questions

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas

The applicability of MFD thrusters to satellite power systems

The high power self field MPD thruster uses electromagnetic forces rather than electrostatic to accelerate a neutral plasma. The most attractive application of MPD thrusters to satellite power systems is in the area of electric propulsion for a cargo orbit transfer vehicle (COTV). Calculations were performed in order to compare the performance of a COTV using an ion or MPD propulsion system. Results show that the MPD propulsion system gives a shorter trip time with the same power and payload when compared to the ion thruster propulsion system at either value of specific impulse. More important than the trip time benefit may be the advantage a MPD propulsion system provides in system simplicity. Another interesting COTV concept using MPD thrusters is the use of a remote power supply located on the Earth, at GEO, or somewhere in between to transmit power to the COTV in a microwave transmission. The specific impulse at thrust levels of tens of newtons makes a MPD propulsion system a candidate for stationkeeping and attitude control of large space structures such as a SPS

Batch means and spectral variance estimators in Markov chain Monte Carlo

Calculating a Monte Carlo standard error (MCSE) is an important step in the statistical analysis of the simulation output obtained from a Markov chain Monte Carlo experiment. An MCSE is usually based on an estimate of the variance of the asymptotic normal distribution. We consider spectral and batch means methods for estimating this variance. In particular, we establish conditions which guarantee that these estimators are strongly consistent as the simulation effort increases. In addition, for the batch means and overlapping batch means methods we establish conditions ensuring consistency in the mean-square sense which in turn allows us to calculate the optimal batch size up to a constant of proportionality. Finally, we examine the empirical finite-sample properties of spectral variance and batch means estimators and provide recommendations for practitioners.Comment: Published in at http://dx.doi.org/10.1214/09-AOS735 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Student Privacy in Learning Analytics: An Information Ethics Perspective

In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries between information used for assessment and information about how students live and work are blurring. Our goal in this paper is to provide a systematic discussion of the ways in which privacy and learning analytics conflict and to provide a framework for understanding those conflicts. We argue that there are five crucial issues about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. First, we argue that we must distinguish among different entities with respect to whom students have, or lack, privacy. Second, we argue that we need clear criteria for what information may justifiably be collected in the name of learning analytics. Third, we need to address whether purported consequences of learning analytics (e.g., better learning outcomes) are justified and what the distributions of those consequences are. Fourth, we argue that regardless of how robust the benefits of learning analytics turn out to be, students have important autonomy interests in how information about them is collected. Finally, we argue that it is an open question whether the goods that justify higher education are advanced by learning analytics, or whether collection of information actually runs counter to those goods