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•The performance of multi temporal GF-6 images in crop mapping was evaluated.•The two newly added red-edge bands of GF-6 is superior for crop classification.•Mid-March to early-April ...was identified as the crucial temporal window for crop.•Late July was the earliest crop identifiable time with overall accuracy of 90%•GF-6 has great potential for crop mapping in regions with fragmented landscapes.
Accurate and timely crop mapping is crucial for environment assessment, food security and agricultural production. However, for the areas with high landscape heterogeneity and frequent cloudy and rainy weather, the insufficient high-quality satellite images limit the accuracy of crop classification. The recently launched Chinese GF-6 wide field-of-view camera (WFV) with a revisit cycle of 4-day and spatial resolution of 16-meter shows great potential for agricultural monitoring. In this study, Qianjiang City characterized by complex agricultural landscapes was selected as the research area to assess the potential of GF-6 data in identifying crop types. Firstly, the pairwise and global separability were calculated to analyze the effect of different spectral-temporal features of GF-6 images on crop classification. A total of 255 spectral-temporal features derived from 15 GF-6 tiles were then used to perform random forest classification. Furthermore, the classification results were evaluated based on 671 field samples and then compared the accuracy between GF-6 data and Sentinel-2 or Landsat-8 data. In addition, the earliest identifiable time of crop types was also determined by iteratively using all available GF-6 data during each time period. The results suggested that the overall accuracy (OA) of all available GF-6 images was 91.55%, which was significantly higher than that of Landsat-8 data (OA = 85.97%) and was slightly lower than that of Sentinel-2 data (OA = 93.10%). The newly added red-edge bands (0.69 ∼ 0.73 μm, 0.73 ∼ 0.77 μm) and their derivative vegetation indices were important spectral features, and the period from mid-March to early-April was the best temporal window for crop identification in our research area. Moreover, late July was the earliest crop identifiable time with overall accuracy of 90% for the first time of the year. These results indicated the great potential of GF-6 images for classifying crop types in the areas with complex cropping system and fragmented agricultural landscapes, particularly when integrating other satellite data with comparable spatial resolution (e.g. Chinese GF-1 data and Sentinel-2 data).
Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists ...and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space–time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems.
We show that there is substantial heterogeneity in women's labor supply elasticities at the micro level and highlight the implications for aggregate behavior. We consider both intertemporal and ...intratemporal choices, and identify intensive and extensive responses in a consistent life-cycle framework, using US CEX data. Heterogeneity is due to observables, such as age, wealth, hours worked, and the wage level, as well as to unobservable tastes for leisure: the median Marshallian elasticity for hours worked is 0.18, with corresponding Hicksian elasticity of 0.54 and Frisch elasticity of 0.87. At the 90th percentile, these values are 0.79, 1.16, and 1.92. Responses at the extensive margin explain about 54% of the total labor supply response for women under 30, although this declines with age. Aggregate elasticities are higher in recessions, and increase with the length of the recession. The heterogeneity at the micro level means that the aggregate labor supply elasticity is not a structural parameter: any aggregate elasticity will depend on the demographic structure of the economy as well as the distribution of wealth and the particular point in the business cycle.
Abstract
We show that around any
m
-partite product state
ρ
p
r
o
d
=
ρ
1
⊗
…
⊗
ρ
m
of full rank (that is
d
e
t
(
ρ
p
r
o
d
)
≠
0
)
, there exists a finite-sized closed ball of separable states ...centered around
ρ
p
r
o
d
whose radius is
β
:=
2
1
−
m
/
2
λ
m
i
n
(
ρ
p
r
o
d
)
. Here,
λ
m
i
n
(
ρ
p
r
o
d
)
is the smallest eigenvalue of
ρ
p
r
o
d
. We are assuming that the total Hilbert space is finite dimensional and we use the notion of distance induced by the Frobenius norm. Applying a scaling relation, we also give a new and simple sufficient criterion for multipartite separability based on trace:
T
r
ρ
ρ
p
r
o
d
2
/
T
r
ρ
2
⩾
T
r
ρ
p
r
o
d
2
−
β
2
. Using the separable balls around the full-rank product states, we discuss the existence and possible sizes of separable balls around any multipartite separable states, which are important features for the set of all separable states. We discuss the implication of these separable balls on entanglement dynamics.
For a Tychonoff space X, Cp(X) is the space of all real-valued continuous functions with the topology of pointwise convergence. A subset A⊂X is said to be sequentially dense in X if every point of X ...is the limit of a convergent sequence in A. In this paper, the following 8 properties for Cp(X) are considered.S1(S,S)⇒Sfin(S,S)⇒S1(S,D)⇒Sfin(S,D)⇑⇑⇑⇑S1(D,S)⇒Sfin(D,S)⇒S1(D,D)⇒Sfin(D,D)
For example, a space X satisfies S1(D,S) (resp., Sfin(D,S)) if whenever {Dn:n∈N} is a sequence of dense subsets of X, one can take points xn∈Dn (resp., finite Fn⊂Dn) such that {xn:n∈N} (resp., ⋃{Fn:n∈N}) is sequentially dense in X. Other properties are defined similarly. S1(D,D) (=R-separability) and Sfin(D,D) (=M-separability) for Cp(X) were already investigated by several authors. In this paper, we have gave characterizations for Cp(X) to satisfy other 6 properties above.
The performance of a support vector machine (SVM) depends highly on the selection of the kernel function type and relevant parameters. To choose the kernel parameters properly, methods analyzing the ...class separability have been widely adopted for their efficiency compared with other methods, such as the popular grid search algorithm. This paper proposes a novel index called the Expected Square Distance Ratio (ESDR), which can serve as a better class separability criterion than the existing ones. Experiments on real-world datasets show that, compared with common kernel parameter selection methods that utilize the between-class separation, the variations in ESDR with respect to the kernel parameter are much more in line with those of the classification accuracy, leading to better kernel parameters. Moreover, ESDR takes the exact data distribution into account and can thus be used to study the model selection problem of an SVM for certain forms of data distribution. As an example, we employ the ESDR to analyze the selection of RBF (Radial Basis Function) kernel parameters for Gaussian data classification.
Many criteria for statistical parameter estimation, such as maximum likelihood, are formulated as a nonlinear optimization problem. Automatic Differentiation Model Builder (ADMB) is a programming ...framework based on automatic differentiation, aimed at highly nonlinear models with a large number of parameters. The benefits of using AD are computational efficiency and high numerical accuracy, both crucial in many practical problems. We describe the basic components and the underlying philosophy of ADMB, with an emphasis on functionality found in no other statistical software. One example of such a feature is the generic implementation of Laplace approximation of high-dimensional integrals for use in latent variable models. We also review the literature in which ADMB has been used, and discuss future development of ADMB as an open source project. Overall, the main advantages of ADMB are flexibility, speed, precision, stability and built-in methods to quantify uncertainty.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
A group G is called residually finite if for every non-trivial element g∈G, there exists a finite quotient Q of G such that the element g is non-trivial in the quotient as well. Instead of just ...investigating whether a group satisfies this property, a new perspective is to quantify residual finiteness by studying the minimal size of the finite quotient Q depending on the complexity of the element g, for example by using the word norm ‖g‖G if the group G is assumed to be finitely generated. The residual finiteness growth RFG:N→N is then defined as the smallest function such that if ‖g‖G≤r, there exists a morphism φ:G→Q to a finite group Q with |Q|≤RFG(r) and φ(g)≠eQ.
Although upper bounds have been established for several classes of groups, exact asymptotics for the function RFG are only known for very few groups such as abelian groups, the Grigorchuk group and certain arithmetic groups. In this paper, we show that the residual finiteness growth of virtually abelian groups equals logk for some k∈N, where the value k is given by an explicit expression. As an application, we show that for every m≥1 and every 1≤k≤m, there exists a group G containing a normal abelian subgroup of rank m and with RFG≈logk.
The study of higher-dimensional black holes is a subject which has recently attracted vast interest. Perhaps one of the most surprising discoveries is a realization that the properties of ...higher-dimensional black holes with the spherical horizon topology and described by the Kerr–NUT–(A)dS metrics are very similar to the properties of the well known four-dimensional Kerr metric. This remarkable result stems from the existence of a single object called the principal tensor. In our review we discuss explicit and hidden symmetries of higher-dimensional Kerr–NUT–(A)dS black hole spacetimes. We start with discussion of the Killing and Killing–Yano objects representing explicit and hidden symmetries. We demonstrate that the principal tensor can be used as a “seed object” which generates all these symmetries. It determines the form of the geometry, as well as guarantees its remarkable properties, such as special algebraic type of the spacetime, complete integrability of geodesic motion, and separability of the Hamilton–Jacobi, Klein–Gordon, and Dirac equations. The review also contains a discussion of different applications of the developed formalism and its possible generalizations.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Soft topology studies a structure on the collection of all soft sets on a given set of alternatives (the relevant attributes being fixed). It is directly inspired by the axioms of a topological ...space. This paper contributes to the theoretical bases of soft topology in various ways. We extend a general construction of soft topologies from topologies on the set of alternatives in two different directions. An extensive discussion with criteria about what a soft counterpart of “topological separability” should satisfy is also given. The interactions of the properties that arise with separability, and of second-countability and its soft counterpart, are studied under the general mechanisms that generate soft topological spaces. The first non-trivial examples of soft second-countable soft topological spaces are produced as a consequence.
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