Looking at the Lanham Act: Images in Trademark and Advertising Law

Words are the prototypical regulatory subjects for trademark and advertising law, despite our increasingly audiovisual economy. This word-focused baseline means that the Lanham Act often misconceives its object, resulting in confusion and incoherence. This Article explores some of the ways courts have attempted to fit images into a word-centric model, while not fully recognizing the particular ways in which images make meaning in trademark and other forms of advertising. While problems interpreting images are likely to persist, this Article suggests some ways in which courts could pay closer attention to the special features of images as compared to words

Inductive Knowledge

This paper formulates some paradoxes of inductive knowledge. Two responses in particular are explored: According to the first sort of theory, one is able to know in advance that certain observations will not be made unless a law exists. According to the other, this sort of knowledge is not available until after the observations have been made. Certain natural assumptions, such as the idea that the observations are just as informative as each other, the idea that they are independent, and that they increase your knowledge monotonically (among others) are given precise formulations. Some surprising consequences of these assumptions are drawn, and their ramifications for the two theories examined. Finally, a simple model of inductive knowledge is offered, and independently derived from other principles concerning the interaction of knowledge and counterfactuals

Identifying Virtues and Values Through Obituary Data-Mining

Because obituaries are succinct and explicitly intended to summarize their subjects’ lives, they may be expected to include only the features that the author finds most salient but also to signal to others in the community the socially-recognized aspects of the deceased’s character. We begin by reviewing studies 1 and 2, in which obituaries were carefully read and labeled. We then report study 3, which further develops these results with a semi-automated, large-scale semantic analysis of several thousand obituaries. Geography, gender, and elite status all turn out to be associated with the virtues and values associated with the deceased

Contours of Inclusion: Frameworks and Tools for Evaluating Arts in Education

This collection of essays explores various arts education-specific evaluation tools, as well as considers Universal Design for Learning (UDL) and the inclusion of people with disabilities in the design of evaluation instruments and strategies. Prominent evaluators Donna M. Mertens, Robert Horowitz, Dennie Palmer Wolf, and Gail Burnaford are contributors to this volume. The appendix includes the AEA Standards for Evaluation. (Contains 10 tables, 2 figures, 30 footnotes, and resources for additional reading.) This is a proceedings document from the 2007 VSA arts Research Symposium that preceded the American Evaluation Association's (AEA) annual meeting in Baltimore, MD

Chapter I - Landing in Japan

In which our three heroes -- William E. Griffis, missionary; Edward S. Morse, scientist; and Lafcadio Hearn, writer -- find during their first weeks in Japan that this Asian country lives up to some of their preconceptions, violates others, and altogether proves to be a more complicated, perplexing, challenging, and interesting place than they had imagined

Estimating Extinction using Unsupervised Machine Learning

Dust extinction is the most robust tracer of the gas distribution in the interstellar medium, but measuring extinction is limited by the systematic uncertainties involved in estimating the intrinsic colors to background stars. In this paper we present a new technique, PNICER, that estimates intrinsic colors and extinction for individual stars using unsupervised machine learning algorithms. This new method aims to be free from any priors with respect to the column density and intrinsic color distribution. It is applicable to any combination of parameters and works in arbitrary numbers of dimensions. Furthermore, it is not restricted to color space. Extinction towards single sources is determined by fitting Gaussian Mixture Models along the extinction vector to (extinction-free) control field observations. In this way it becomes possible to describe the extinction for observed sources with probability densities. PNICER effectively eliminates known biases found in similar methods and outperforms them in cases of deep observational data where the number of background galaxies is significant, or when a large number of parameters is used to break degeneracies in the intrinsic color distributions. This new method remains computationally competitive, making it possible to correctly de-redden millions of sources within a matter of seconds. With the ever-increasing number of large-scale high-sensitivity imaging surveys, PNICER offers a fast and reliable way to efficiently calculate extinction for arbitrary parameter combinations without prior information on source characteristics. PNICER also offers access to the well-established NICER technique in a simple unified interface and is capable of building extinction maps including the NICEST correction for cloud substructure. PNICER is offered to the community as an open-source software solution and is entirely written in Python.Comment: Accepted for publication in A&A, source code available at http://smeingast.github.io/PNICER