Riang-Lang vocabulary: compiled from the materials collected by G. H. Luce

This is a facsimile edition of the Riang-Lang vocabulary prepared by late Prof. Harry Shorto in 1964, and which had until now only circulated privately as rather poor quality photocopies. Prof. Shorto complied the vocabulary in the context of preparing the first draft of his A Mon-Khmer Comparative Dictionary (posthumously published in 2006 by Pacific Linguistics), by compiling and analyzing data from the extensive notes of Gordon H. Luce (now archived in the manuscript collection of the Australian National Library). Luce’s notes were in turn based on his own field work in Burma and on the (now lost) substantial index card compilation of Prof. Otto Blagden, who preceded both Shorto and Luce at the University of London’s School of Oriental and African studies. The entries in this Riang-Lang are written in Shorto’s phonemic transcription, they are glossed in English, and are richly augmented with etymological commentary that includes citations from Shan, Burmese, many Austroasiatic languages, and Shorto’s preliminary Proto-Mon-Khmer reconstruction. The present word list was found among boxes kept by his daughter Anna, which she kindly donated to the present series editor (Sidwell) for the purposes of publication and archiving. The images were created by scanning the original pages at 200 DPI in greyscaleAustralian National University, College of Asia and the Pacifi

Comparisons of refractive index gradient and stability profiles measured by balloons and the MU radar at a high vertical resolution in the lower stratosphere

Many experimental studies have demonstrated that VHF Stratosphere-Troposphere (ST) radar echo power is proportional to the generalized refractive index gradient squared <I>M</I><sup>2</sup> when using a vertically oriented beam. Because humidity is generally negligible above the tropopause, VHF ST radars can thus provide information on the static stability (quantified by the squared Brunt-Väisälä frequency <I>N</I><sup>2</sup>) at stratospheric heights and this capability is useful for many scientific applications. Most studies have been performed until now at a vertical resolution of 150 m or more. In the present paper, results of comparisons between radar- and (balloon borne) radiosonde-derived <I>M</I><sup>2</sup> and <I>N</I><sup>2</sup> are shown at a better vertical resolution of 50 m with the MU radar (34.85° N, 136.15° E; Japan) by benefiting from the range resolution improvement provided by the multi-frequency range imaging technique, using the Capon processing method. Owing to favorable winds in the troposphere, the radiosondes did not drift horizontally more than about 30 km from the MU radar site by the time they reached an altitude of 20 km. The measurements were thus simultaneous and almost collocated. Very good agreements have been obtained between both high resolution profiles of <i>M</i><sup>2</sup>, as well as profiles of <i>N</i><sup>2</sup>. It is also shown that this agreement can still be improved by taking into account a frozen-in advection of the air parcels by a horizontally uniform wind. Therefore, it can be concluded that 1) the range imaging technique with the Capon method really provides substantial range resolution improvement, despite the relatively weak Signal-to-Noise Ratios (SNR) over the analyzed region of the lower stratosphere, 2) the proportionality of the radar echo power to <I>M</I><sup>2</sup> at a vertical scale down to 50 m in the lower stratosphere is experimentally demonstrated, 3) the MU radar can provide stability profiles with a vertical resolution of 50 m at heights where humidity is negligible, 4) stable stratospheric layers as thin as 50 m or less have at least a horizontal extent of a few km to several tens of kilometers and can be considered as frozenly advected over scales of a few tens of minutes

Investigation of inverse design of multilayer thin-films with conditional invertible Neural Networks

The task of designing optical multilayer thin-films regarding a given target is currently solved using gradient-based optimization in conjunction with methods that can introduce additional thin-film layers. Recently, Deep Learning and Reinforcement Learning have been been introduced to the task of designing thin-films with great success, however a trained network is usually only able to become proficient for a single target and must be retrained if the optical targets are varied. In this work, we apply conditional Invertible Neural Networks (cINN) to inversely designing multilayer thin-films given an optical target. Since the cINN learns the energy landscape of all thin-film configurations within the training dataset, we show that cINNs can generate a stochastic ensemble of proposals for thin-film configurations that that are reasonably close to the desired target depending only on random variables. By refining the proposed configurations further by a local optimization, we show that the generated thin-films reach the target with significantly greater precision than comparable state-of-the art approaches. Furthermore, we tested the generative capabilities on samples which are outside the training data distribution and found that the cINN was able to predict thin-films for out-of-distribution targets, too. The results suggest that in order to improve the generative design of thin-films, it is instructive to use established and new machine learning methods in conjunction in order to obtain the most favorable results

Microminiaturized, biopotential conditioning system (MBCS)

Multichannel, medical monitoring system allows almost complete freedom of movement for subject during monitoring periods. System comprises monitoring unit (biobelt), transmission line, and data acquisition unit. Belt, made of polybenzimidizole fabric, is wrapped around individual's waist and held in place by overlapping sections of Velcro closure material

Take Me Out In A Velie Car

https://digitalcommons.library.umaine.edu/mmb-vp/6353/thumbnail.jp

Extracting topological features from dynamical measures in networks of Kuramoto oscillators

The Kuramoto model for an ensemble of coupled oscillators provides a paradigmatic example of non-equilibrium transitions between an incoherent and a synchronized state. Here we analyze populations of almost identical oscillators in arbitrary interaction networks. Our aim is to extract topological features of the connectivity pattern from purely dynamical measures, based on the fact that in a heterogeneous network the global dynamics is not only affected by the distribution of the natural frequencies, but also by the location of the different values. In order to perform a quantitative study we focused on a very simple frequency distribution considering that all the frequencies are equal but one, that of the pacemaker node. We then analyze the dynamical behavior of the system at the transition point and slightly above it, as well as very far from the critical point, when it is in a highly incoherent state. The gathered topological information ranges from local features, such as the single node connectivity, to the hierarchical structure of functional clusters, and even to the entire adjacency matrix.Comment: 11 pages, 10 figure

Reply to Comment by Jonathan J. Rhodes on ‘‘Modeling of the Interactions between Forest Vegetation, Disturbances, and Sediment Yields’’

Rhodes [2005] brings up some excellent points in his comments on the work of Istanbulluoglu et al. [2004]. We appreciate the opportunity to respond because it is likely that other readers will also wonder how they can apply the relatively simple analysis to important policy questions. Models necessarily reduce the complexity of the problem to make it tractable and synthesize some diverse sources of information. It may be helpful at times for readers to understand the high dimension of the complexity sacrificed in order to obtain the synthesis and the reasons for reducing the complexity in a particular manner. Rhodes [2005] comments on three things: (1) the omission of roads and landings from the analysis; (2) the implicit assumption that fire does not occur with harvesting; and (3) the overestimation of water repellency. We will respond to each of these, clarifying and elaborating on the basis for our modeling choices