On the genus Anchonus Schönherr in Florida (Coleoptera: Curculionidae)

Four species of Anchonus Schonherr occur in Florida: A. flol'idanus Schwarz, A. dul'yi Blatchley, A. blatchleyi Sleeper, and A. suillus (Fabricius), which is recorded from Florida and the continental United States for the first time. The species are distinguished in a key and illustrated. A lectotype is selected for A. floridanus

Fidelity of photon propagation in electromagnetically induced transparency in the presence of four-wave mixing

We study the effects of the four-wave mixing (4WM) in a quantum memory scheme based on electromagnetically induced transparency (EIT). We treat the problem of field propagation on the quantum mechanical level, which allows us to calculate the fidelity for propagation for a quantum light pulse such as a single photon. While 4WM can be beneficial for classical, all-optical information storage, the quantum noise associated with the signal amplification and idler generation is in general detrimental for a quantum memory. We identify a range of parameters where 4WM makes a single photon quantum memory impossible

Effectiveness of Ninth-Grade Physics in Maine: Conceptual Understanding

The Physics First movement - teaching a true physics course to ninth grade students - is gaining popularity in high schools. There are several different rhetorical arguments for and against this movement, and it is quite controversial in physics education. However, there is no actual evidence to assess the success, or failure, of this substantial shift in the science teaching sequence. We have undertaken a comparison study of physics classes taught in ninth- and 12th grade classes in Maine. Comparisons of student understanding and gains with respect to mechanics concepts were made with excerpts from well-known multiple-choice surveys and individual student interviews. Results indicate that both populations begin physics courses with similar content knowledge and specific difficulties, but that in the learning of the concepts ninth graders are more sensitive to the instructional method used.Comment: 15 pages, 2 tables, 0 figures, to be published in The Physics Teache

Locally Estimating Core Numbers

Graphs are a powerful way to model interactions and relationships in data from a wide variety of application domains. In this setting, entities represented by vertices at the "center" of the graph are often more important than those associated with vertices on the "fringes". For example, central nodes tend to be more critical in the spread of information or disease and play an important role in clustering/community formation. Identifying such "core" vertices has recently received additional attention in the context of {\em network experiments}, which analyze the response when a random subset of vertices are exposed to a treatment (e.g. inoculation, free product samples, etc). Specifically, the likelihood of having many central vertices in any exposure subset can have a significant impact on the experiment. We focus on using $k$-cores and core numbers to measure the extent to which a vertex is central in a graph. Existing algorithms for computing the core number of a vertex require the entire graph as input, an unrealistic scenario in many real world applications. Moreover, in the context of network experiments, the subgraph induced by the treated vertices is only known in a probabilistic sense. We introduce a new method for estimating the core number based only on the properties of the graph within a region of radius $\delta$ around the vertex, and prove an asymptotic error bound of our estimator on random graphs. Further, we empirically validate the accuracy of our estimator for small values of $\delta$ on a representative corpus of real data sets. Finally, we evaluate the impact of improved local estimation on an open problem in network experimentation posed by Ugander et al.Comment: Main paper body is identical to previous version (ICDM version). Appendix with additional data sets and enlarged figures has been added to the en

Modelling Ireland’s exchange rates: from EMS to EMU

This paper attempts to model the nominal and real exchange rate for Ireland, relative to Germany and the UK from 1975 to 2003. It offers an overview of the theory of purchasing power parity (PPP), focusing particularly on likely sources of nonlinearity. Potential difficulties in placing the analysis in the standard I(1)/I(0) framework are highlighted and comparisons with previous Irish studies are made. Tests for fractional integration and nonlinearity, including random field regressions, are discussed and applied. The results obtained highlight the likely inadequacies of the standard cointegration and STAR approaches to modelling, and point instead to multiple structural changes models. Using this approach, both bilateral nominal exchange rates are effectively modelled, and in the case of Ireland and Germany, PPP is found to be valid not only in the long run, but also in the medium term. JEL Classification: C22, C51, F31, F41fractional Dickey-Fuller tests, multiple structural changes models, purchasing power parity, random field regression, smooth transition autoregression

Demand for Money: A Study in Testing Time Series for Long Memory and Nonlinearity

This paper draws attention to the limitations of the standard unit root/cointegration approach to economic and financial modelling, and to some of the alternatives based on the idea of fractional integration, long memory models, and the random field regression approach to nonlinearity. Following brief explanations of fractional integration and random field regression, and the methods of applying them, selected techniques are applied to a demand for money dataset. Comparisons of the results from this illustrative case study are presented, and conclusions are drawn that should aid practitioners in applied time-series econometrics.

Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study

This paper draws attention to the limitations of the standard unit root/cointegration approach to economic and financial modelling, and to some of the alternatives based on the idea of fractional integration, long memory models, and the random field regression approach to nonlinearity. Following brief explanations of fractional integration and random field regression, and the methods of applying them, selected techniques are applied to a demand for money dataset. Comparisons of the results from this illustrative case study are presented, and conclusions are drawn that should aid practitioners in applied time-series econometrics.

Purchasing Power Parity: The Irish Experience Re-visited

This paper looks at issues surrounding the testing of purchasing power parity using Irish data. Potential difficulties in placing the analysis in an I(1)/I(0) framework are highlighted. Recent tests for fractional integration and nonlinearity are discussed and used to investigate the behaviour of the Irish exchange rate against the United Kingdom and Germany. Little evidence of fractionality is found but there is strong evidence of nonlinearity from a variety of tests. Importantly, when the nonlinearity is modelled using a random field regression, the data conform well to purchasing power parity theory, in contrast to the findings of previous Irish studies, whose results were very mixed.