Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient

Propagation Characteristics Of Density Currents And Implications To Pollutant Transport In A Stratified Reservoir

With global warming, the frequency and intensity of extreme rainfall events were predicted to change more dramatically in the near future while the amount of total precipitation will change slightly. Large volume of turbid inflow will enter the source water reservoir after a heavy rainfall, and evolve in various types of density currents depending on the density difference between the inflow and background water. Density currents play an important role in the thermal structure and pollutant transport in the reservoir. Understanding the behaviors of density current is fundamental to study the changes of source water quality during the flooding season. Characteristics of density currents were first experimentally investigated in a pilot stratified reservoir with a length of 2.0m and a depth of 0.54m, in which the thermal stratification was achieved with a heating method. When the stratification stability indexes were of 0.0112～0.0197 m-1 and the buoyancy frequencies were of 0.3314～0.4393 s-1, the turbid inflow was observed to separate from the bed slope and to propagate horizontally into its equilibrium layer, namely interflow. The separation depth of density currents and the thickness of the interflow were both smaller in the strong stratification cases than those in the weak cases, which had an important impact on the pollutant transport in the reservoir. Propagation characteristics of density currents and its implications to pollutant transport were systemically explored by numerically simulating behaviors of density currents under different conditions of stratification stability index, inflow velocity and sediment content of inflow. After careful calibration of Euler-Euler model, the simulated separation depth of density currents and the thickness of the interflow agreed well with the experimental ones, which showed the propagation of inflow was closely related to the stratification level. Impacts of inflow velocity and sediment content of inflow on the propagation of density currents were different under the simulated conditions. When the volume fraction of sediment in the inflow was increased from 0.025% to 0.20%, the separation depth of density currents was decreased from 21.0cm to 18.5cm, the thickness of the interflow was slightly increased from 6.2cm to 7.8cm, but the heights of the internal hydraulic jump were almost the same. The inflow velocity mainly influenced the time of developing the interflow, the developing time decreased as the inflow velocity increased, which implied the water quality would deteriorate quickly after a heavy rainfall. Under larger inflow velocity conditions, mixing between the inflow and background water was stronger due to the higher energy carried by the inflow, and this caused the larger depth of interflow and the bigger height of internal hydraulic jump, which indicated the pollutants carried by turbid inflow would be transported more widely

Measuring the boundary gapless state and criticality via disorder operator

The disorder operator is often designed to reveal the conformal field theory (CFT) information in the quantum many-body system. By using large-scale quantum Monte Carlo simulation, we study the scaling behavior of disorder operator on the boundary in the two-dimensional Heisenberg model on the square-octagon lattice with gapless topological edge state. In the Affleck-Kennedy-Lieb-Tasaki (AKLT) phase, the disorder operator is shown to hold the perimeter scaling with a logarithmic term associated with the Luttinger Liquid parameter K. This effective Luttinger Liquid parameter K reflects the low energy physics and CFT for (1+1)d boundary. At bulk critical point, the effective K is suppressed but keep finite value, indicating the coupling between the gapless edge state and bulk fluctuation. The logarithmic term numerically capture this coupling picture, which reveals the (1+1)d SU(2)_1 CFT and (2+1)d O(3) CFT at boundary criticality. Our work paves a new way to study the exotic boundary state and boundary criticality.Comment: 8 Pages,7 figure

Mass-induced sea level change in the northwestern North Pacific and its contribution to total sea level change

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 40 (2013): 3975–3980, doi:10.1002/grl.50748.Over the period 2003–2011, the Gravity Recovery and Climate Experiment (GRACE) satellite pair revealed a remarkable variability in mass-induced sea surface height (MSSH) in the northwestern North Pacific. A significant correlation is found between MSSH and observed total sea surface height (SSH), indicative of the importance of barotropic variability in this region. For the period 2003–2011, MSSH rose at a rate of 6.1 ± 0.7 mm/yr, which has a significant contribution to the SSH rise (8.3 ± 0.7 mm/yr). Analysis of the barotropic vorticity equation based on National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis product, GRACE, and altimetry data suggests that the MSSH signal is primarily caused by negative wind stress curl associated with an anomalous anticyclonic atmospheric circulation. Regression analysis indicates that trends in MSSH and surface wind are related to the Pacific Decadal Oscillation, whose index had a decreasing trend in the last decade.This work was supported by the National Basic Research Program of China (2010CB950303 and 2012CB955603) and the National Natural Science Foundation of China (41176023, 41276108, and 41006006). X.H.C. is also sponsored by “Youth Innovation Promotion Association,” CAS (SQ201204, LTOZZ1202).2014-02-0

Observational constraints on cosmic neutrinos and dark energy revisited

Using several cosmological observations, i.e. the cosmic microwave background anisotropies (WMAP), the weak gravitational lensing (CFHTLS), the measurements of baryon acoustic oscillations (SDSS+WiggleZ), the most recent observational Hubble parameter data, the Union2.1 compilation of type Ia supernovae, and the HST prior, we impose constraints on the sum of neutrino masses (\mnu), the effective number of neutrino species (\neff) and dark energy equation of state ($w$), individually and collectively. We find that a tight upper limit on \mnu can be extracted from the full data combination, if \neff and $w$ are fixed. However this upper bound is severely weakened if \neff and $w$ are allowed to vary. This result naturally raises questions on the robustness of previous strict upper bounds on \mnu, ever reported in the literature. The best-fit values from our most generalized constraint read \mnu=0.556^{+0.231}_{-0.288}\rm eV, \neff=3.839\pm0.452, and $w=-1.058\pm0.088$ at 68% confidence level, which shows a firm lower limit on total neutrino mass, favors an extra light degree of freedom, and supports the cosmological constant model. The current weak lensing data are already helpful in constraining cosmological model parameters for fixed $w$. The dataset of Hubble parameter gains numerous advantages over supernovae when $w=-1$, particularly its illuminating power in constraining \neff. As long as $w$ is included as a free parameter, it is still the standardizable candles of type Ia supernovae that play the most dominant role in the parameter constraints.Comment: 39 pages, 15 figures, 7 tables, accepted to JCA

Environmental regulation effects from the perspective on the industrial chain: evidence from energy enterprises in China

More attention has been paid to environmental regulation of greenhouse gas emissions in the energy industry under the transformation of industrial structure. This paper takes microdata of Chinese energy enterprises from 1998 to 2012 as a sample to build a duty-sharing model, analyzes the effect of environmental regulations on the industrial chain, and explains the “double growth” phenomenon that occurred in China, which is nothing short of miraculous in terms of the environment and economy. In the industrial chain, the environmental obligations and responsibilities will be shared between upstream and downstream enterprises due to trade linkages. This paper finds that environmental responsibilities will move forward through the industrial chain when environmental regulations are strengthened. Downstream companies will loosen “relative” control constraints, thereby expanding output but increasing demand for upstream products. Different from the existing research, we claim that, since environmental regulation has a differential effect on the industrial chain, it will promote the growth of output in the entire chain, in contrast to the theory of “cost compliance”, which claims that environmental regulation will inevitably lead to the output. Based on this research, this paper puts forward some suggestions and insights on how the government implements environmental regulations

Video Event Recognition by Dempster-Shafer Theory

Abstract. This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method em-ploying evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high-level events with semantic meaning along with de-grees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78 % and 100 % for four composite events are encouraging.

Electrophysiological properties of heteromeric TRPV4–C1 channels

AbstractWe previously reported that TRPV4 and TRPC1 can co-assemble to form heteromeric TRPV4–C1 channels [12]. In the present study, we characterized some basic electrophysiological properties of heteromeric TRPV4–C1 channels. 4α-Phorbol 12,13-didecanoate (4α-PDD, a TRPV4 agonist) activated a single channel current in HEK293 cells co-expressing TRPV4 and TRPC1. The activity of the channels was abrogated by a TRPC1-targeting blocking antibody T1E3. Conductance of the channels was ~95pS for outward currents and ~83pS for inward currents. The channels with similar conductance were also recorded in cells expressing TRPV4–C1 concatamers, in which assembled channels were expected to be mostly 2V4:2C1. Fluorescence Resonance Energy Transfer (FRET) experiments confirmed the formation of a protein complex with 2V4:2C1 stoichiometry while suggesting an unlikeliness of 3V4:1C1 or 1V4:3C1 stoichiometry. Monovalent cation permeability profiles were compared between heteromeric TRPV4–C1 and homomeric TRPV4 channels. For heteromeric TRPV4–C1 channels, their permeation profile was found to fit to Eisenman sequence VI, indicative of a strong field strength cation binding site, whereas for homomeric TRPV4 channels, their permeation profile corresponded to Eisenman sequence IV for a weak field strength binding site. Compared to homomeric TRPV4 channels, heteromeric TRPV4–C1 channels were slightly more permeable to Ca2+ and had a reduced sensitivity to extracellular Ca2+ inhibition. In summary, we found that, when TRPV4 and TRPC1 were co-expressed in HEK293 cells, the predominate assembly type was 2V4:2C1. The heteromeric TRPV4–C1 channels display distinct electrophysiological properties different from those of homomeric TRPV4 channels