The Orbital Period of V368 Aquilae (Nova Aquilae 1936 No. 2)

We report observations of the eclipsing classical nova V368 Aql (Nova Aql 1936 No. 2). These data reveal that the orbital period previously published by Diaz & Bruch is an alias of the true orbital period. A total of 14 eclipses (12 complete and 2 partial) over 25 nights of observation have established that the orbital period of V368 Aql is 0.6905093(1) d (16.57 hr), which is roughly twice the previously published period. With its revised orbital period, V368 Aql now joins other nova systems with periods in excess of 0.5 day that dominate the long end of the orbital period distribution of cataclysmic variables.Comment: Accepted for publication in the PAS

Corporate Hierarchies and the Size of Nations: Theory and Evidence

Corporate organization varies within a country and across countries with country size. The paper starts by establishing some facts about corporate organization based on unique data of 660 Austrian and German corporations. The larger country (Germany) has larger firms with flatter more decentral corporate hierarchies compared to the smaller country (Austria). Firms in the larger country change their organization less fast than firms in the smaller country. Over time firms have been introducing less hierarchical organizations by delegating power to lower levels of the corporation. We develop a theory which explains these facts and which links these features to the trade environment that countries and firms face. We introduce firms with internal hierarchies in a Krugman (1980) model of trade. We show that international trade and the toughness of competition in international markets induce a power struggle in firms which eventually leads to decentralized corporate hierarchies. We offer econometric evidence which is consistent with the models predictions

Quantum Theory of a Resonant Photonic Crystal

We present a quantum model of two-level atoms localized in a 3D lattice, based on the Hopfield theory of exciton polaritons. In addition to a polaritonic gap at the exciton energy, a photonic bandgap opens up at the Brillouin zone boundary. Upon tuning the lattice period or angle of incidence to match the photonic gap with the exciton energy, one obtains a combined polaritonic and photonic gap as a generalization of Rabi splitting. For typical experimental parameters, the size of the combined gap is on the order of 25 cm^{-1}, up to 10^5 times the detuned gap size. The dispersion curve contains a branch supporting slow-light modes with vanishing exciton probability density.Comment: 4 pages, 3 figure

Art Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data

A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on the fuzzy ARTMAP neural network, is developed. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for vegetation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, as well as back propagation neural networks and K Nearest Neighbor algorithms. ARTMAP dynamics are fast, stable, and scalable, overcoming common limitations of back propagation, which did not give satisfactory performance. Best results are obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. A prototype remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automatically constructs a minimal number of recognition categories to meet accuracy criteria. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-l-0409, N00014-95-0657

Nanoscale assembly processes revealed in the nacroprismatic transition zone of Pinna nobilis mollusc shells

Intricate biomineralization processes in molluscs engineer hierarchical structures with meso-, nano-, and atomic architectures that give the final composite material exceptional mechanical strength and optical iridescence on the macroscale. This multiscale biological assembly inspires new synthetic routes to complex materials. Our investigation of the prism-nacre interface reveals nanoscale details governing the onset of nacre formation using high-resolution scanning transmission electron microscopy. A wedge polishing technique provides unprecedented, large-area specimens required to span the entire interface. Within this region, we find a transition from nanofibrillar aggregation to irregular early-nacre layers, to well-ordered mature nacre suggesting the assembly process is driven by aggregation of nanoparticles (~50-80 nm) within an organic matrix that arrange in fiber-like polycrystalline configurations. The particle number increases successively and, when critical packing is reached, they merge into early-nacre platelets. These results give new insights into nacre formation and particle-accretion mechanisms that may be common to many calcareous biominerals.Comment: 5 Figure

The European Union: The Transfer of Trust and Loyalty in a Meritocracy via the Five Waves of Enlargement

The European Union (EU) faces a new wave of populism that opposes the traditional meritocratic system and highlights the breach of trust between citizens and civil servants. The breakdown in the citizens’ trust for national and supranational institutions occurred at the same time as the loyalty of the civil servants diminished. This paper examines whether the rising populist movement can explicate the decline of trust and loyalty. Further, it highlights the five waves of EU enlargement to discern where and why the breach in trust is occurring. Utilizing information collected by the Eurobarometer, this paper uses new data that break down the statistics into the five key waves of EU enlargement and examines the notion of enlargement fatigue. The data explore the financial and societal influences that alter one\u27s perspective of national and supranational institutions. Further, each Member State’s position within the EU is evaluated in the context of assumed two-tiered membership, which is emphasized via the different waves of enlargement. The theoretical lens employed is neo-functionalism, as it provides an opportunity to examine the relationships between trust, loyalty, and enlargement through a new perspective. This paper concludes that populist movements are exploiting anxieties to promote their agenda, that citizens’ trust in institutions is actually increasing, and that there is a correlation between low trust and new, unstable Member States. Overall, in the face of populist movements, local conflicts and media frenzies, European citizens most fervently desire a reliable governmental system for future generations

ART and ARTMAP Neural Networks for Applications: Self-Organizing Learning, Recognition, and Prediction

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems. Applications include parts design retrieval at the Boeing Company, automatic mapping from remote sensing satellite measurements, medical database prediction, and robot vision. This chapter features a self-contained introduction to ART and ARTMAP dynamics and a complete algorithm for applications. Computational properties of these networks are illustrated by means of remote sensing and medical database examples. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, that allows the network to encode important rare cases but that may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. In medical database prediction problems, which often feature inconsistent training input predictions, the ARTMAP-IC network further improves ARTMAP performance with distributed prediction, category instance counting, and a new search algorithm. A recently developed family of ART models (dART and dARTMAP) retains stable coding, recognition, and prediction, but allows arbitrarily distributed category representation during learning as well as performance.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-1-0409, N00014-95-0657