Compositional loess modeling

Cleveland (1979) is usually credited with the introduction of the locally weighted regression, Loess. The concept was further developed by Cleveland and Devlin (1988). The general idea is that for an arbitrary number of explanatory data points xi the value of a dependent variable is estimated ^yi. The ^yi is the tted value from a dth degree polynomial in xi. (In practice often d = 1.) The ^yi is tted using weighted least squares, WLS, where the points xk (k = 1; : : : ; n) closest to xi are given the largest weights. We de ne a weighted least squares estimation for compositional data, C-WLS. In WLS the sum of the weighted squared Euclidean distances between the observed and the estimated values is minimized. In C-WLS we minimize the weighted sum of the squared simplicial distances (Aitchison, 1986, p. 193) between the observed compositions and their estimates. We then de ne a compositional locally weighted regression, C-Loess. Here a composition is assumed to be explained by a real valued (multivariate) variable. For an arbitrary number of data points xi we for each xi t a dth degree polynomial in xi yielding an estimate ^yi of the composition yi. We use C-WLS to t the polynomial giving the largest weights to the points xk (k = 1; : : : ; n) closest to xi. Finally the C-Loess is applied to Swedish opinion poll data to create a poll-of-polls time series. The results are compared to previous results not acknowledging the compositional structure of the data

Economic efficiency of pig breeding schemes. Aspects on a national two breeds program

International audienc

Selection indexes for beef cattle

International audienc

Flights in my hands : coherence concerns in designing Strip'TIC, a tangible space for air traffic controllers

Best Paper Honorable Mention awardInternational audienceWe reflect upon the design of a paper-based tangible interactive space to support air traffic control. We have observed, studied, prototyped and discussed with controllers a new mixed interaction system based on Anoto, video projection, and tracking. Starting from the understanding of the benefits of tangible paper strips, our goal is to study how mixed physical and virtual augmented data can support the controllers' mental work. The context of the activity led us to depart from models that are proposed in tangible interfaces research where coherence is based on how physical objects are representative of virtual objects. We propose a new account of coherence in a mixed interaction system that integrates externalization mechanisms. We found that physical objects play two roles: they act both as representation of mental objects and as tangible artifacts for interacting with augmented features. We observed that virtual objects represent physical ones, and not the reverse, and, being virtual representations of physical objects, should seamlessly converge with the cognitive role of the physical object. Finally, we show how coherence is achieved by providing a seamless interactive space

The Mechanical Behavior of a 25Cr Super Duplex Stainless Steel at Elevated Temperature

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Comparative chromosome painting discloses homologous Segments in distantly related mammals

Comparative chromosome painting, termed ZOO-FISH, using DNA libraries from flow sorted human chromosomes 1,16,17 and X, and mouse chromosome 11 discloses the presence of syntenic groups in distantly related mammalian Orders ranging from primates (Homo sapiens), rodents (Mus musculus), even-toed ungulates (Muntiacus muntjak vaginalis and Muntiacus reevesi) and whales (Balaenoptera physalus). These mammalian Orders have evolved separately for 55-80 million years (Myr). We conclude that ZOO-FISH can be used to generate comparative chromosome maps of a large number of mammalian species

Transformation of Quartz to Tridymite in the Presence of Binary Silicate Liquids

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66429/1/j.1151-2916.1967.tb15146.x.pd

Uncertainty in United States coastal wetland greenhouse gas inventorying

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Environmental Research Letters 13 (2018): 115005, doi:10.1088/1748-9326/aae157.Coastal wetlands store carbon dioxide (CO2) and emit CO2 and methane (CH4) making them an important part of greenhouse gas (GHG) inventorying. In the contiguous United States (CONUS), a coastal wetland inventory was recently calculated by combining maps of wetland type and change with soil, biomass, and CH4 flux data from a literature review. We assess uncertainty in this developing carbon monitoring system to quantify confidence in the inventory process itself and to prioritize future research. We provide a value-added analysis by defining types and scales of uncertainty for assumptions, burial and emissions datasets, and wetland maps, simulating 10 000 iterations of a simplified version of the inventory, and performing a sensitivity analysis. Coastal wetlands were likely a source of net-CO2-equivalent (CO2e) emissions from 2006–2011. Although stable estuarine wetlands were likely a CO2e sink, this effect was counteracted by catastrophic soil losses in the Gulf Coast, and CH4 emissions from tidal freshwater wetlands. The direction and magnitude of total CONUS CO2e flux were most sensitive to uncertainty in emissions and burial data, and assumptions about how to calculate the inventory. Critical data uncertainties included CH4 emissions for stable freshwater wetlands and carbon burial rates for all coastal wetlands. Critical assumptions included the average depth of soil affected by erosion events, the method used to convert CH4 fluxes to CO2e, and the fraction of carbon lost to the atmosphere following an erosion event. The inventory was relatively insensitive to mapping uncertainties. Future versions could be improved by collecting additional data, especially the depth affected by loss events, and by better mapping salinity and inundation gradients relevant to key GHG fluxes. Social Media Abstract: US coastal wetlands were a recent and uncertain source of greenhouse gasses because of CH4 and erosion.Financial support was provided primarily by NASA Carbon Monitoring Systems (NNH14AY67I) and the USGS Land Carbon Program, with additional support from The Smithsonian Institution, The Coastal Carbon Research Coordination Network (DEB-1655622), and NOAA Grant: NA16NMF4630103