100 research outputs found
Low-Dimensional Representations of Earth System Processes
In times of global change, we must closely monitor the state of our planet in order to understand gradual or abrupt changes early on. In fact, each of the Earth's subsystems-i.e. the biosphere, atmosphere, hydrosphere, cryosphere, and anthroposphere-can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region, e.g. the Multivariate ENSO (El Ăino-Southern Oscillation) Index. Indicator approaches have been used extensively to describe socioeconomic data too, and a range of indices have been proposed to synthesize and interpret this information. For instance the "Human Development Index" (HDI) by the United Nations Development Programme was designed to capture specific aspects of development.
"Dimensionality reduction" (DR) is a widely used approach to find low dimensional and interpretable representations of data that are natively embedded in high-dimensional spaces. Here, we propose a robust method to create indicators using dimensionality reduction to better represent the terrestrial biosphere and the global socioeconomic system. We aim to explore the performance of the approach and to interpret the resulting indicators.
For biosphere indicators, the concept was tested using 12 explanatory variables representing the biophysical states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73% of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions.
We also apply the method on the "World Development Indicators", a database with more than 1500 variables, to track the socioeconomic development at a country level. The aim was to extract the core dimensions of development in a highly efficient way, using a method of nonlinear dimensionality reduction. We find that over 90% of variance in the WDIs can be represented by five uncorrelated and nonlinear dimensions. The first dimension (explaining 74%) represents the state of education, health, income, infrastructure, trade, population, and pollution. The second dimension (explaining 10%) differentiates countries by gender ratios, labor market, and energy production patterns. Overall, we find that the data contained in the WDIs are highly nonlinear therefore requiring nonlinear methods to extract the main patterns of development. Globally, most countries show rather consistent temporal trends towards wealthier and aging societies. Deviations from the long-term trajectories are detected with our approach during warfare, environmental disasters, or fundamental political changes.
In general we find that the indicator approach is able to extract general patterns from complex databases and that it can be applied to databases of varying characteristics. We also find that indicators are can different kinds of changes occurring in the system, such as extreme events, permanent changes or trends. Therefore it is a useful tool for general monitoring and exploratory data analysis. The approach is flexible and can be applied to complex datasets, such as large data, nonlinear data, as well as data with many missing values.In times of global change, we must closely monitor the state of our planet in order to understand gradual or abrupt changes early on. In fact, each of the Earth's subsystems-i.e. the biosphere, atmosphere, hydrosphere, cryosphere, and anthroposphere-can be analyzed from a multitude of data streams. However, since it is very hard to jointly interpret multiple monitoring data streams in parallel, one often aims for some summarizing indicator. Climate indices, for example, summarize the state of atmospheric circulation in a region, e.g. the Multivariate ENSO (El Ăino-Southern Oscillation) Index. Indicator approaches have been used extensively to describe socioeconomic data too, and a range of indices have been proposed to synthesize and interpret this information. For instance the "Human Development Index" (HDI) by the United Nations Development Programme was designed to capture specific aspects of development.
"Dimensionality reduction" (DR) is a widely used approach to find low dimensional and interpretable representations of data that are natively embedded in high-dimensional spaces. Here, we propose a robust method to create indicators using dimensionality reduction to better represent the terrestrial biosphere and the global socioeconomic system. We aim to explore the performance of the approach and to interpret the resulting indicators.
For biosphere indicators, the concept was tested using 12 explanatory variables representing the biophysical states of ecosystems and land-atmosphere water, energy, and carbon fluxes. We find that two indicators account for 73% of the variance of the state of the biosphere in space and time. While the first indicator summarizes productivity patterns, the second indicator summarizes variables representing water and energy availability. Anomalies in the indicators clearly identify extreme events, such as the Amazon droughts (2005 and 2010) and the Russian heatwave (2010), they also allow us to interpret the impacts of these events. The indicators also reveal changes in the seasonal cycle, e.g. increasing seasonal amplitudes of productivity in agricultural areas and in arctic regions.
We also apply the method on the "World Development Indicators", a database with more than 1500 variables, to track the socioeconomic development at a country level. The aim was to extract the core dimensions of development in a highly efficient way, using a method of nonlinear dimensionality reduction. We find that over 90% of variance in the WDIs can be represented by five uncorrelated and nonlinear dimensions. The first dimension (explaining 74%) represents the state of education, health, income, infrastructure, trade, population, and pollution. The second dimension (explaining 10%) differentiates countries by gender ratios, labor market, and energy production patterns. Overall, we find that the data contained in the WDIs are highly nonlinear therefore requiring nonlinear methods to extract the main patterns of development. Globally, most countries show rather consistent temporal trends towards wealthier and aging societies. Deviations from the long-term trajectories are detected with our approach during warfare, environmental disasters, or fundamental political changes.
In general we find that the indicator approach is able to extract general patterns from complex databases and that it can be applied to databases of varying characteristics. We also find that indicators are can different kinds of changes occurring in the system, such as extreme events, permanent changes or trends. Therefore it is a useful tool for general monitoring and exploratory data analysis. The approach is flexible and can be applied to complex datasets, such as large data, nonlinear data, as well as data with many missing values
Quantum dark solitons in Bose gas confined in a hard wall box
Schr\"odinger equation for Bose gas with repulsive contact interactions in
one-dimensional space may be solved analytically with the help of the Bethe
ansatz if we impose periodic boundary conditions. It was shown that in such a
system there exist many-body eigenstates directly corresponding to dark soliton
solutions of the mean-field equation. The system is still integrable if one
switches from the periodic boundary conditions to an infinite square well
potential. The corresponding eigenstates were constructed by M. Gaudin. We
analyze weak interaction limit of Gaudin's solutions and identify
parametrization of eigenstates strictly connected with single and multiple dark
solitons. Numerical simulations of detection of particle's positions reveal
dark solitons in the weak interaction regime and their quantum nature in the
presence of strong interactions.Comment: 7 pages, 4 figures, version accepted for publication in Phys. Rev.
Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients
Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd-sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co-occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70-year period. The direct comparison of the two data sets reveals that the crowd-sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real-time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd-sourced biodiversity data can effectively guide conservation measures
A Deep Chandra ACIS Study of NGC 4151. III. the Line Emission and Spectral Analysis of the Ionization Cone
This paper is the third in a series in which we present deep Chandra ACIS-S
imaging spectroscopy of the Seyfert 1 galaxy NGC 4151, devoted to study its
complex circum-nuclear X-ray emission. Emission features in the soft X-ray
spectrum of the bright extended emission (L[0.3-2keV]~10^40 erg/s) at r>130 pc
(2") are consistent with the brighter OVII, OVIII, and NeIX lines seen in the
Chandra HETGS and XMM-Newton RGS spectra below 2 keV. We construct emission
line images of these features and find good morphological correlations with the
narrow line region clouds mapped in [OIII]5007A. Self-consistent
photoionization models provide good descriptions of the spectra of the large
scale emission, as well as resolved structures, supporting the dominant role of
nuclear photoionization, although displacement of optical and X-ray features
implies a more complex medium. Collisionally ionized emission is estimated to
be <12% of the extended emission. Presence of both low and high ionization
spectral components and extended emission in the X-ray image perpendicular to
the bicone indicates leakage of nuclear ionization, likely filtered through
warm absorbers, instead of being blocked by a continuous obscuring torus. The
ratios of [OIII]/soft X-ray flux are approximately constant (~15) for the 1.5
kpc radius spanned by these measurements, indicating a relatively constant
ionization parameter, consistent with the photoionized outflow of a wind-like
density profile. Using spatially resolved features, we estimate that the mass
outflow rate in NGC 4151 is ~2Msun/yr at 130 pc and the kinematic power of the
ionized outflow is 1.7x10^41 erg/s, approximately 0.3% of the bolometric
luminosity of NGC 4151.Comment: 45 pages. 18 figures. Accepted to Ap
Imputing missing data in plant traits: A guide to improve gapâfilling
Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, âgap-fillingâ approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets
using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage.
Innovation: We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), (2) correlations (bi-and multivariate) and (3) taxonomic and functional clustering (valuewise, uni-and
multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data setâexternal trait data had little effect on induced taxonomic clustering and stabilized traitâtrait correlations.
Main Conclusions: Our study extends the criteria for the evaluation of gap-filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation
A Multiwavelength Study of Young Massive Star-Forming Regions. III. Mid-Infrared Emission
We present mid-infrared (MIR) observations, made with the TIMMI2 camera on
the ESO 3.6 m telescope, toward 14 young massive star-forming regions. All
regions were imaged in the N band, and nine in the Q band, with an angular
resolution of ~ 1 arcsec. Typically, the regions exhibit a single or two
compact sources (with sizes in the range 0.008-0.18 pc) plus extended diffuse
emission. The Spitzer-Galactic Legacy Infrared Mid-Plane Survey Extraordinaire
images of these regions show much more extended emission than that seen by
TIMMI2, and this is attributed to polycyclic aromatic hydrocarbon (PAH) bands.
For the MIR sources associated with radio continuum radiation (Paper I) there
is a close morphological correspondence between the two emissions, suggesting
that the ionized gas (radio source) and hot dust (MIR source) coexist inside
the H II region. We found five MIR compact sources which are not associated
with radio continuum emission, and are thus prime candidates for hosting young
massive protostars. In particular, objects IRAS 14593-5852 II (only detected at
17.7 microns) and 17008-4040 I are likely to be genuine O-type protostellar
objects. We also present TIMMI2 N-band spectra of eight sources, all of which
are dominated by a prominent silicate absorption feature (~ 9.7 microns). From
these data we estimate column densities in the range (7-17)x10^22 cm^-2, in
good agreement with those derived from the 1.2 mm data (Paper II). Seven
sources show bright [Ne II] line emission, as expected from ionized gas
regions. Only IRAS 123830-6128 shows detectable PAH emission at 8.6 and 11.3
microns.Comment: Published in ApJ. 15 pages, 6 figures. Formatted with emulateapj; v2:
Minor language changes to match the published versio
A Deep Chandra ACIS Study of NGC 4151. II. The Innermost Emission Line Region and Strong Evidence for Radio Jet-NLR Cloud Collision
We have studied the X-ray emission within the inner 150 pc radius of NGC 4151
by constructing high spatial resolution emission line images of OVII, OVIII,
and NeIX. These maps show extended structures that are spatially correlated
with the radio outflow and optical [OIII] emission. We find strong evidence for
jet--gas cloud interaction, including morphological correspondences with
regions of X-ray enhancement, peaks of near-infrared [FeII] emission, and
optical clouds. In these regions, moreover, we find evidence of elevated
NeIX/OVII ratios; the X-ray emission of these regions also exceeds that
expected from nuclear photoionization. Spectral fitting reveals the presence of
a collisionally ionized component. The thermal energy of the hot gas suggests
that >0.1% of the estimated jet power is deposited into the host interstellar
medium through interaction between the radio jet and the dense medium of the
circum-nuclear region. We find possible pressure equilibrium between the
collisionally ionized hot gas and the photoionized line-emitting cool clouds.
We also obtain constraints on the extended iron and silicon fluorescent
emission. Both lines are spatially unresolved. The upper limit on the
contribution of an extended emission region to the Fe Kalpha emission is <5% of
the total, in disagreement with a previous claim that 65% of the Fe Kalpha
emission originates in the extended narrow line region.Comment: Accepted for publication in ApJ. 28 pages, 9 figure
A Deep Chandra ACIS Study of NGC 4151. I. the X-ray Morphology of the 3 kpc-diameter Circum-nuclear Region and Relation to the Cold Interstellar Medium
We report on the imaging analysis of 200 ks sub-arcsecond resolution Chandra
ACIS-S observations of the nearby Seyfert 1 galaxy NGC 4151. Bright, structured
soft X-ray emission is observed to extend from 30 pc to 1.3 kpc in the
south-west from the nucleus, much farther than seen in earlier X-ray studies.
The terminus of the north-eastern X-ray emission is spatially coincident with a
CO gas lane, where the outflow likely encounters dense gas in the host galactic
disk. X-ray emission is also detected outside the boundaries of the ionization
cone, which indicates that the gas there is not completely shielded from the
nuclear continuum, as would be the case for a molecular torus collimating the
bicone. In the central r<200 pc region, the subpixel processing of the ACIS
data recovers the morphological details on scales of <30~pc (<0.5") first
discovered in Chandra HRC images. The X-ray emission is more absorbed towards
the boundaries of the ionization cone, as well as perpendicular to the bicone
along the direction of a putative torus in NGC 4151. The innermost region where
X-ray emission shows the highest hardness ratio, is spatially coincident with
the near-infrared resolved H_2 emission and dusty spirals we find in an HST V-H
color image. The agreement between the observed H_2 line flux and the value
predicted from X-ray-irradiated molecular cloud models supports
photo-excitation by X-rays from the active nucleus as the origin of the H_2
line, although contribution from UV fluorescence or collisional excitation
cannot be fully ruled out with current data. The discrepancy between the mass
of cold molecular gas inferred from recent CO and near-infrared H_2
observations may be explained by the anomalous CO abundance in this X-ray
dominated region. The total H_2 mass derived from the X-ray observation agrees
with measurement in Storchi-Bergmann et al.Comment: 33 pages, 9 figures and 2 table
Pinning quantum phase transition for a Luttinger liquid of strongly interacting bosons
One of the most remarkable results of quantum mechanics is the fact that
many-body quantum systems may exhibit phase transitions even at zero
temperature. Quantum fluctuations, deeply rooted in Heisenberg's uncertainty
principle, and not thermal fluctuations, drive the system from one phase to
another. Typically, the relative strength of two competing terms in the
system's Hamiltonian is changed across a finite critical value. A well-known
example is the Mott-Hubbard quantum phase transition from a superfluid to an
insulating phase, which has been observed for weakly interacting bosonic atomic
gases. However, for strongly interacting quantum systems confined to
lower-dimensional geometry a novel type of quantum phase transition may be
induced for which an arbitrarily weak perturbation to the Hamiltonian is
sufficient to drive the transition. Here, for a one-dimensional (1D) quantum
gas of bosonic caesium atoms with tunable interactions, we observe the
commensurate-incommensurate quantum phase transition from a superfluid
Luttinger liquid to a Mott-insulator. For sufficiently strong interactions, the
transition is induced by adding an arbitrarily weak optical lattice
commensurate with the atomic granularity, which leads to immediate pinning of
the atoms. We map out the phase diagram and find that our measurements in the
strongly interacting regime agree well with a quantum field description based
on the exactly solvable sine-Gordon model. We trace the phase boundary all the
way to the weakly interacting regime where we find good agreement with the
predictions of the 1D Bose-Hubbard model. Our results open up the experimental
study of quantum phase transitions, criticality, and transport phenomena beyond
Hubbard-type models in the context of ultracold gases
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