Density of States for a Specified Correlation Function and the Energy Landscape

The degeneracy of two-phase disordered microstructures consistent with a specified correlation function is analyzed by mapping it to a ground-state degeneracy. We determine for the first time the associated density of states via a Monte Carlo algorithm. Our results are described in terms of the roughness of the energy landscape, defined on a hypercubic configuration space. The use of a Hamming distance in this space enables us to define a roughness metric, which is calculated from the correlation function alone and related quantitatively to the structural degeneracy. This relation is validated for a wide variety of disordered systems.Comment: Accepted for publication in Physical Review Letter

Modeling Heterogeneous Materials via Two-Point Correlation Functions: I. Basic Principles

Heterogeneous materials abound in nature and man-made situations. Examples include porous media, biological materials, and composite materials. Diverse and interesting properties exhibited by these materials result from their complex microstructures, which also make it difficult to model the materials. In this first part of a series of two papers, we collect the known necessary conditions on the standard two-point correlation function S2(r) and formulate a new conjecture. In particular, we argue that given a complete two-point correlation function space, S2(r) of any statistically homogeneous material can be expressed through a map on a selected set of bases of the function space. We provide new examples of realizable two-point correlation functions and suggest a set of analytical basis functions. Moreover, we devise an efficient and isotropy- preserving construction algorithm, namely, the Lattice-Point algorithm to generate realizations of materials from their two- point correlation functions based on the Yeong-Torquato technique. Subsequent analysis can be performed on the generated images to obtain desired macroscopic properties. These developments are integrated here into a general scheme that enables one to model and categorize heterogeneous materials via two-point correlation functions.Comment: 37 pages, 26 figure

Magnetic structure of superconducting Eu(Fe0.82Co0.18)2As2 as revealed by single-crystal neutron diffraction

The magnetic structure of superconducting Eu(Fe0.82Co0.18)2As2 is unambiguously determined by single-crystal neutron diffraction. A long-range ferromagnetic order of the Eu2+ moments along the c-direction is revealed below the magnetic phase transition temperature Tc = 17 K. In addition, the antiferromagnetism of the Fe2+ moments still survives and the tetragonal-to-orthorhombic structural phase transition is also observed, although the transition temperatures of the Fe-spin density wave (SDW) order and the structural phase transition are significantly suppressed to Tn = 70 K and Ts = 90 K, respectively, compared to the parent compound EuFe2As2.We present the microscopic evidences for the coexistence of the Eu-ferromagnetism (FM) and the Fe-SDW in the superconducting crystal. The superconductivity (SC) competes with the Fe-SDW in Eu(Fe0.82Co0.18)2As2.Moreover, the comparison between Eu(Fe1-xCox)2As2 and Ba(Fe1-xCox)2As2 indicates a considerable influence of the rare-earth element Eu on the magnetism of the Fe sublattice.Comment: 7 pages, 7 figures, accepted for publication in Physical Review

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

Effect of Grazing on Soil Fertility and Trace Elements of Temperate Desert Steppe in Northwestern China

Grazing is the dominant land use of temperate desert steppe in northwestern China. It is well established that the grazing process has impacts on plant production and biodiversity (Li 1997), but less is known about the effects of grazing chemical characteristics of grassland soils. Livestock feeding activities, especially the provision of supplementary sources, may influence pasture nutrient cycle, and in turn change the chemical composition of the grassland soil (Rong et al. 2001). Soil fertility may be affected by the type use and grazing intensities because these may cause alterations in soil physical and chemical properties as well as the soil biotia (Marzaioli et al. 2010; Caravacaa et al. 2002). The combination of these factors can reduce pasture productivity (Islam and Weil 2000; Sánchez-Marañón et al. 2002). Soil was the nutrient carrier to forage and livestock. Since changes in soil property will be reflected in both forage and livestock production directly or indirectly, it was important to study the effect of soil on grassland and grazing (Zhang et al. 2002), especially trace elements such as iron (Fe), manganese (Mn), copper (Cu) and zinc (Zn). In Alxa (Inner Mongolia), for example, Wu et al. (2008) reported a difference in the accumulation of these elements due to grassland type with a sequence of Fe\u3eMn\u3eZn\u3eCu in alpine meadow soil compared to a sequence of Fe \u3e Mn\u3eCu\u3eZn in mountainous steppe and desert steppe soils. The objective of this study is to investigate the effect of grazing on the concentrations of Fe, Mn, Cu and Zn in temperate desert steppe in Gansu Province

Generating a Fractal Butterfly Floquet Spectrum in a Class of Driven SU(2) Systems

A scheme for generating a fractal butterfly Floquet spectrum, first proposed by Wang and Gong [Phys. Rev. A {\bf 77}, 031405(R) (2008)], is extended to driven SU(2) systems such as a driven two-mode Bose-Einstein condensate. A new class of driven systems without a link with the Harper model context is shown to have an intriguing butterfly Floquet spectrum. The found butterfly spectrum shows remarkable deviations from the known Hosftadter's butterfly. In addition, the level crossings between Floquet states of the same parity and between Floquet states of different parities are studied and highlighted. The results are relevant to studies of fractal statistics, quantum chaos, coherent destruction of tunneling, as well as the validity of mean-field descriptions of Bose-Einstein condensates.Comment: 11 pages, 9 figures, relatively large size, a full-length report of the findings in arXiv 0906.225

Response of bacterioplankton community structure to an artificial gradient of pCO2 in the Arctic Ocean

In order to test the influences of ocean acidification on the ocean pelagic ecosystem, so far the largest CO2 manipulation mesocosm study (European Project on Ocean Acidification, EPOCA) was performed in Kings Bay (Kongsfjorden), Spitsbergen. During a 30 day incubation, bacterial diversity was investigated using DNA fingerprinting and clone library analysis of bacterioplankton samples. Terminal restriction fragment length polymorphism (T-RFLP) analysis of the PCR amplicons of the 16S rRNA genes revealed that general bacterial diversity, taxonomic richness and community structure were influenced by the variation of productivity during the time of incubation, but not the degree of ocean acidification. A BIOENV analysis suggested a complex control of bacterial community structure by various biological and chemical environmental parameters. The maximum apparent diversity of bacterioplankton (i.e., the number of T-RFs) in high and low pCO2 treatments differed significantly. A negative relationship between the relative abundance of Bacteroidetes and pCO2 levels was observed for samples at the end of the experiment by the combination of T-RFLP and clone library analysis. Our study suggests that ocean acidification affects the development of bacterial assemblages and potentially impacts the ecological function of the bacterioplankton in the marine ecosystem

Effect of Stocking Rate on a \u3cem\u3eStipa Breviflora\u3c/em\u3e Desert Steppe Community of Inner Mongolia

Stocking rate is an important factor in grazing management. The stocking rate defines utilization and ultimately grazing pressure, which in turn affects grassland sustainability. Grassland sustainability is partly defined by its species composition and ultimately by its productivity. These attributes are unique for specific plant communities and the effect of stocking rate must be established for each in order to understand the community response to grazing and to determine its carrying capacity. While some information exists on the effects of stocking rate on livestock production in the Stipa breviflora Griseb. Desert Steppe (Wei et al., 2000), the effects on the plant community are not understood well. This study aimed to determine the effects of stocking rate on the species composition and productivity of that community