The spatial–temporal variation of poverty determinants

Poverty affects many people worldwide and varies in space and time, although its determinants are geographical factors. This paper presents a case study from Hubei Province, Central China, investigating the spatial and temporal changes in poverty determinants at the county from 2013-2019 and village levels from 2013 to 2017. We investigated the variation in the spatial autocorrelation of poverty incidence at the two levels using global and local Moran's I. We then explored the spatial and temporal variations of poverty determinants using the Lineman, Merenda, and Gold method. We found that the overall spatial autocorrelation gradually mitigated, whereas the local spatial pattern remained unchanged at both levels. Deeply poor areas were concentrated in the western part of Hubei Province and the southwestern part of Yunyang County. The effects of geographical conditions on poverty decreased across the study period, with the R2 value decreasing from 85% to 73% at the county level and from 57% to 38% at the village level. Furthermore, the contribution of natural environmental factors to poverty slightly decreased at both scale levels, whereas the socioeconomic factors had a significantly increased effect on county-level poverty over time. By contrast, the factors that have a major effect on village-level poverty remained stable. The results might indicate that the implementation of various targeted poverty alleviation measures since 2013 have mitigated the restrictions of local geographical factors on poverty alleviation.</p

Geoscience-aware deep learning:A new paradigm for remote sensing

Information extraction is a key activity for remote sensing images. A common distinction exists between knowledge-driven and data-driven methods. Knowledge-driven methods have advanced reasoning ability and interpretability, but have difficulty in handling complicated tasks since prior knowledge is usually limited when facing the highly complex spatial patterns and geoscience phenomena found in reality. Data-driven models, especially those emerging in machine learning (ML) and deep learning (DL), have achieved substantial progress in geoscience and remote sensing applications. Although DL models have powerful feature learning and representation capabilities, traditional DL has inherent problems including working as a black box and generally requiring a large number of labeled training data. The focus of this paper is on methods that integrate domain knowledge, such as geoscience knowledge and geoscience features (GK/GFs), into the design of DL models. The paper introduces the new paradigm of geoscience-aware deep learning (GADL), in which GK/GFs and DL models are combined deeply to extract information from remote sensing data. It first provides a comprehensive summary of GK/GFs used in GADL, which forms the basis for subsequent integration of GK/GFs with DL models. This is followed by a taxonomy of approaches for integrating GK/GFs with DL models. Several approaches are detailed using illustrative examples. Challenges and research prospects in GADL are then discussed. Developing more novel and advanced methods in GADL is expected to become the prevailing trend in advancing remotely sensed information extraction in the future.</p

Spatial Association from the Perspective of Mutual Information

Measures of spatial association are important to reveal the spatial structures and patterns in geographical phenomena. They have utility for spatial interpolation, stochastic simulation, and causal inference, among others. Such measures are abundantly available for continuous spatial variables, whereas for categorical spatial variables they are less well developed. In this research, we developed a measure of spatial association for categorical spatial variables coined the entropogram, quantifying its spatial association using mutual information. Mutual information concerns information shared by pairs of random variables at different locations as revealed by their observed joint frequency distribution and marginal frequency distributions. The developed new measure is modeled as a function of lag in analogy to the variogram. Whereas existing measures focus mainly on interstate relationships, the entropogram models the spatial correlation in categorical spatial variables holistically. In this way, the entropogram imparts multiple advantages, for example, simplifying the representation of spatial structure for categorical variables and facilitating communication. The entropogram also reflects variation in the spatial correlation between different states. We first explored the properties of the entropogram in a simulation study. Then, we applied the entropogram to analyze the spatial association of land cover types in Qinxian, Shanxi, China. We conclude that the entropogram provides a suitable addition to existing measures of spatial association for applications in a wide range of disciplines where the categorical spatial variable is of interest.</p

Computing Topology Preservation of RBF Transformations for Landmark-Based Image Registration

In image registration, a proper transformation should be topology preserving. Especially for landmark-based image registration, if the displacement of one landmark is larger enough than those of neighbourhood landmarks, topology violation will be occurred. This paper aim to analyse the topology preservation of some Radial Basis Functions (RBFs) which are used to model deformations in image registration. Mat\'{e}rn functions are quite common in the statistic literature (see, e.g. \cite{Matern86,Stein99}). In this paper, we use them to solve the landmark-based image registration problem. We present the topology preservation properties of RBFs in one landmark and four landmarks model respectively. Numerical results of three kinds of Mat\'{e}rn transformations are compared with results of Gaussian, Wendland's, and Wu's functions

Delusional beliefs and reason giving

Delusions are often regarded as irrational beliefs, but their irrationality is not sufficient to explain what is pathological about them. In this paper we ask whether deluded subjects have the capacity to support the content of their delusions with reasons, that is, whether they can author their delusional states. The hypothesis that delusions are characterised by a failure of authorship, which is a dimension of self knowledge, deserves to be empirically tested because (a) it has the potential to account for the distinction between endorsing a delusion and endorsing a framework belief; (b) it contributes to a philosophical analysis of the relationship between rationality and self knowledge; and (c) it informs diagnosis and therapy in clinical psychiatry. However, authorship cannot provide a demarcation criterion between delusions and other irrational belief states

Bcl3 prevents acute inflammatory lung injury in mice by restraining emergency granulopoiesis

Granulocytes are pivotal regulators of tissue injury. However, the transcriptional mechanisms that regulate granulopoiesis under inflammatory conditions are poorly understood. Here we show that the transcriptional coregulator B cell leukemia/lymphoma 3 (Bcl3) limits granulopoiesis under emergency (i.e., inflammatory) conditions, but not homeostatic conditions. Treatment of mouse myeloid progenitors with G-CSF — serum concentrations of which rise under inflammatory conditions — rapidly increased Bcl3 transcript accumulation in a STAT3-dependent manner. Bcl3-deficient myeloid progenitors demonstrated an enhanced capacity to proliferate and differentiate into granulocytes following G-CSF stimulation, whereas the accumulation of Bcl3 protein attenuated granulopoiesis in an NF-κB p50–dependent manner. In a clinically relevant model of transplant-mediated lung ischemia reperfusion injury, expression of Bcl3 in recipients inhibited emergency granulopoiesis and limited acute graft damage. These data demonstrate a critical role for Bcl3 in regulating emergency granulopoiesis and suggest that targeting the differentiation of myeloid progenitors may be a therapeutic strategy for preventing inflammatory lung injury

Regularity for harmonic maps into certain Pseudo-Riemannian manifolds

In this article, we investigate the regularity for certain elliptic systems without a $L^2$-antisymmetric structure. As applications, we prove some $\epsilon$-regularity theorems for weakly harmonic maps from the unit ball $B= B(m) \subset \mathbb{R}^m$ $(m\geq2)$ into certain pseudo-Riemannian manifolds: standard stationary Lorentzian manifolds, pseudospheres $\mathbb{S}^n_\nu \subset \mathbb{R}^{n+1}_\nu$ $(1\leq\nu \leq n)$ and pseudohyperbolic spaces $\mathbb{H}^n_\nu \subset \mathbb{R}^{n+1}_{\nu+1}$ $(0\leq\nu \leq n-1)$. Consequently, such maps are shown to be H\"{o}lder continuous (and as smooth as the regularity of the targets permits) in dimension $m=2$. In particular, we prove that any weakly harmonic map from a disc into the De-Sitter space $\mathbb{S}^n_1$ or the Anti-de-Sitter space $\mathbb{H}^n_1$ is smooth. Also, we give an alternative proof of the H\"{o}lder continuity of any weakly harmonic map from a disc into the Hyperbolic space $\mathbb{H}^n$ without using the fact that the target is nonpositively curved. Moreover, we extend the notion of generalized (weakly) harmonic maps from a disc into the standard sphere $\mathbb{S}^n$ to the case that the target is $\mathbb{S}^n_\nu$ $(1\leq\nu \leq n)$ or $\mathbb{H}^n_\nu$ $(0\leq\nu \leq n-1)$, and obtain some $\epsilon$-regularity results for such generalized (weakly) harmonic maps.Comment: to appear in J. Math. Pures App

Presenilin-Dependent Receptor Processing Is Required for Axon Guidance

SummaryThe Alzheimer's disease-linked gene presenilin is required for intramembrane proteolysis of amyloid-β precursor protein, contributing to the pathogenesis of neurodegeneration that is characterized by loss of neuronal connections, but the role of Presenilin in establishing neuronal connections is less clear. Through a forward genetic screen in mice for recessive genes affecting motor neurons, we identified the Columbus allele, which disrupts motor axon projections from the spinal cord. We mapped this mutation to the Presenilin-1 gene. Motor neurons and commissural interneurons in Columbus mutants lacking Presenilin-1 acquire an inappropriate attraction to Netrin produced by the floor plate because of an accumulation of DCC receptor fragments within the membrane that are insensitive to Slit/Robo silencing. Our findings reveal that Presenilin-dependent DCC receptor processing coordinates the interplay between Netrin/DCC and Slit/Robo signaling. Thus, Presenilin is a key neural circuit builder that gates the spatiotemporal pattern of guidance signaling, thereby ensuring neural projections occur with high fidelity

Representations of an integer by some quaternary and octonary quadratic forms

In this paper we consider certain quaternary quadratic forms and octonary quadratic forms and by using the theory of modular forms, we find formulae for the number of representations of a positive integer by these quadratic forms.Comment: 20 pages, 4 tables. arXiv admin note: text overlap with arXiv:1607.0380