Functional Decomposition Using Majority

Typical operators for the decomposition of Boolean functions in state-of-the-art algorithms are AND, exclusive-OR (XOR), and a 2-to-1 multiplexer (MUX). We propose a logic decomposition algorithm that uses the majority-of-three (MAJ) operation. Such decomposition can extend the capabilities of current logic decomposition, but only found limited attention in previous work. Our algorithm makes use of a decomposition rule based on MAJ. Combined with disjoint-support decomposition, the algorithm can factorize XOR-Majority Graphs (XMGs), a recently proposed data structure which has XOR, MAJ, and inverters as only logic primitives. XMGs have been applied in various applications, including (i) exact synthesis aware rewriting, (ii) pre-optimization for 6-LUT mapping, and (iii) synthesis of quantum networks. An experimental evaluation shows that our algorithm leads to better XMGs compared to state-of-the-art algorithms, which positively affect all these three applications. As one example, our experiments show that the proposed method achieves up to 37.1% with an average of 9.6% reduction on the look-up tables (LUT) size/depth product applied to the EPFL arithmetic benchmarks after technology mapping

Hierarchical Variation in Cellulose Decomposition Across Southern Ontario Reference Streams

The cotton strip assay (CSA) has been promoted as a method of incorporating functional indicators into standardized river assessments by study of organic-matter decomposition. However, operationalization of the CSA as an indicator requires testing the spatial and temporal controls of variance associated with decomposition of cotton strips at regional reference sites. I conducted a hierarchically structured study of decomposition rates in Ontario, Canada. Cotton strips were deployed during the spring, summer and fall in pool and riffle habitats of 22 streams located in three distinct physiographic regions. Partitioning of variation among hierarchical scales associated with rates of decomposition were examined using nested ANOVAs, and comparisons of regional, habitat and seasonal differences were studied using a linear mixed effects model (LMEM). A partial least squares (PLS) regression analysis was performed to identify environmental variables associated with decomposition patterns. I found that variance associated with each successive spatial scale was seasonally dependent, and that temperature was the source of the majority of seasonal distribution of variance. I also found that temperature was be the primary environmental controls of decomposition. By quantifying the natural heterogeneity in decomposition rates, this study will inform biomonitoring practices, enabling progress towards inclusion of the CSA in regional monitoring programs

Hierarchical Multi-resolution Mesh Networks for Brain Decoding

We propose a new framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs), which establishes a set of brain networks at multiple time resolutions of fMRI signal to represent the underlying cognitive process. The suggested framework, first, decomposes the fMRI signal into various frequency subbands using wavelet transforms. Then, a brain network, called mesh network, is formed at each subband by ensembling a set of local meshes. The locality around each anatomic region is defined with respect to a neighborhood system based on functional connectivity. The arc weights of a mesh are estimated by ridge regression formed among the average region time series. In the final step, the adjacency matrices of mesh networks obtained at different subbands are ensembled for brain decoding under a hierarchical learning architecture, called, fuzzy stacked generalization (FSG). Our results on Human Connectome Project task-fMRI dataset reflect that the suggested HMMN model can successfully discriminate tasks by extracting complementary information obtained from mesh arc weights of multiple subbands. We study the topological properties of the mesh networks at different resolutions using the network measures, namely, node degree, node strength, betweenness centrality and global efficiency; and investigate the connectivity of anatomic regions, during a cognitive task. We observe significant variations among the network topologies obtained for different subbands. We, also, analyze the diversity properties of classifier ensemble, trained by the mesh networks in multiple subbands and observe that the classifiers in the ensemble collaborate with each other to fuse the complementary information freed at each subband. We conclude that the fMRI data, recorded during a cognitive task, embed diverse information across the anatomic regions at each resolution.Comment: 18 page

Decomposition of coarse woody debris in a long-term litter manipulation experiment: A focus on nutrient availability

The majority of above-ground carbon in tropical forests is stored in wood, which is returned to the atmosphere during decomposition of coarse woody debris. However, the factors controlling wood decomposition have not been experimentally manipulated over time scales comparable to the length of this process.We hypothesized that wood decomposition is limited by nutrient availability and tested this hypothesis in a long-term litter addition and removal experiment in a lowland tropical forest in Panama. Specifically, we quantified decomposition using a 15-year chronosequence of decaying boles, and measured respiration rates and nutrient limitation of wood decomposer communities.The long-term probability that a dead tree completely decomposed was decreased in plots where litter was removed, but did not differ between litter addition and control treatments. Similarly, respiration rates of wood decomposer communities were greater in control treatments relative to litter removal plots; litter addition treatments did not differ from either of the other treatments. Respiration rates increased in response to nutrient addition (nitrogen, phosphorus, and potassium) in the litter removal and addition treatments, but not in the controls.Established decreases in concentrations of soil nutrients in litter removal plots and increased respiration rates in response to nutrient addition suggest that reduced rates of wood decomposition after litter removal were caused by decreased nutrient availability. The effects of litter manipulations differed directionally from a previous short-term decomposition study in the same plots, and reduced rates of bole decomposition in litter removal plots did not emerge until after more than 6 years of decomposition. These differences suggest that litter-mediated effects on nutrient dynamics have complex interactions with decomposition over time

Synergistic degradation of lignocellulose by fungi and bacteria in boreal forest soil

Thesis (M.S.) University of Alaska Fairbanks, 2015Boreal forests contain an estimated 28% of the world's soil carbon, and currently act as a significant global carbon sink. Plant-derived lignocellulose is a major component of soil carbon, and its decomposition is dependent on soil bacteria and fungi. In order to predict the fate of this soil carbon and its potential feedbacks to climate change, the identities, activity, and interactions of soil microbial decomposer communities must be better understood. This study used stable isotope probing (SIP) with ¹³C-labeled lignocellulose and two of its constituents, cellulose and vanillin, to identify microbes responsible for the processing of lignocellulose-derived carbon and examine the specific roles that they perform. Results indicate that multiple taxa are involved in lignocellulose processing, and that certain taxa target specific portions of the lignocellulose macromolecule; specifically, fungi dominate the degradation of lignocellulose and cellulose macromolecules, while bacteria scavenge aromatic lignocellulose monomers. Major fungal taxa involved in lignocellulose degradation include Ceratobasidium, Geomyces, and Sebacina, among others. Bacterial taxa processing lignocellulose and cellulose included Cellvibrio and Mesorhizobium in high abundance relative to other taxa, although Burkholderia were the primary vanillin consumers. These results elucidate some of the major players in lignocellulose decomposition and their specific roles in boreal forest soil. This information provides knowledge of small-scale microbial processes that dictate ecosystem-level carbon cycling, and can assist in predictions of the fate of boreal forest carbon stocks

Thermal behavior of native, washed and steam exploded lignocellulosic biomass samples

The aim of this study was to evaluate the chemical changes in the main components (cellulose, hemicellulose and lignin) of various lignocellulosic biomass samples during the steam explosion pretreatment. Pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) and thermogravimetry/mass spectrometry (TG/MS) measurements have been performed on different native, washed and steam exploded woody (willow and spruce) and herbaceous (hemp, wheat straw and sweet sorghum bagasse) biomass samples. The main differences between the thermal decomposition of the samples are interpreted in terms of the altered structure of the biomass samples by the effective steam explosion treatment and the different alkali ion contents which have been determined using inductively coupled plasma-optical emission spectroscopy (ICP-OES) method. In order to separate these two effects, the native biomass samples have been washed with hot water to remove the main parts of the potassium and sodium ions. The concentration of K+ and Na+ has been reduced in the treated biomass samples so the thermal decomposition mechanism has been altered due to the elimination of the catalytic effects. Principal Component Analysis (PCA) has been used to find statistical correlations between the data. The functional group compositions of the lignin molecules have been modified significantly as indicated by the pyrograms and the score plot of the PCA. The amount of hemicellulose has been reduced. On the other hand, the relative amount of the structurally modified cellulose has been increased in the samples by the steam explosion pretreatment step

An Empirical Examination of Traditional Neighborhood Development

This study analyzes the impact of the new urbanism on single-family home prices. Specifically, we explore the price differential that homebuyers pay for houses in new urbanist developments relative to houses in conventional suburban developments. Using data on over 5,000 single-family home sales from 1994 to 1997 in three different neighborhoods, hedonic regression results reveal that consumers pay more for homes in new urbanist communities than those in conventional suburban developments. Further analyses indicate that the price premium is not attributable to differences in improvement age and other housing characteristics