Adaptive Sparse Domain Selection for Weather Radar Superresolution

Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a superresolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the precollected data of model weather radar echo patches. Second, the most relevant subdictionaries are adaptively select for each low-resolution echo patches during the spare coding. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.

Adaptive Sparse Domain Selection for Weather Radar Super-Resolution using Decision Support System

Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a super-resolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the pre-collected data of model weather radar echo patches. Second, the most relevant sub-dictionaries are adaptively select for each low-resolution echo patches during the spare coding using a complex decision support system. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.

Drosophila Fezf functions as a transcriptional repressor to direct layer-specific synaptic connectivity in the fly visual system

The layered compartmentalization of synaptic connections, a common feature of nervous systems, underlies proper connectivity between neurons and enables parallel processing of neural information. However, the stepwise development of layered neuronal connections is not well understood. The medulla neuropil of the Drosophila visual system, which comprises 10 discrete layers (M1 to M10), where neural computations underlying distinct visual features are processed, serves as a model system for understanding layered synaptic connectivity. The first step in establishing layer-specific connectivity in the outer medulla (M1 to M6) is the innervation by lamina (L) neurons of one of two broad, primordial domains that will subsequently expand and transform into discrete layers. We previously found that the transcription factor dFezf cell-autonomously directs L3 lamina neurons to their proper primordial broad domain before they form synapses within the developing M3 layer. Here, we show that dFezf controls L3 broad domain selection through temporally precise transcriptional repression of the transcription factor slp1 (sloppy paired 1). In wild-type L3 neurons, slp1 is transiently expressed at a low level during broad domain selection. When dFezf is deleted, slp1 expression is up-regulated, and ablation of slp1 fully rescues the defect of broad domain selection in dFezf-null L3 neurons. Although the early, transient expression of slp1 is expendable for broad domain selection, it is surprisingly necessary for the subsequent L3 innervation of the M3 layer. DFezf thus functions as a transcriptional repressor to coordinate the temporal dynamics of a transcriptional cascade that orchestrates sequential steps of layer-specific synapse formation.

Domain restriction in child Mandarin: Implications for quantifier spreading

This study explores Mandarin children’s competence with quantifier domain restriction. We present results from two experiments in which adults and four- to five-year old children evaluated two possible candidates for the domain selection associated with the distributive operator dou ‘all’ in Mandarin Chinese. In the first experiment, we investigated whether children and adults are capable of selecting an appropriate domain when two candidate NPs both appear inside dou’s quantification scope; i.e., both of the NPs c-command dou. In the second experiment, still two candidate NPs were presented, but one within dou’s scope and the other outside its scope. Our results indicate that four- to five-year-old children are capable of basic distributive computation associated with dou, but they may choose an NP that adults do not usually choose as the domain of dou, resulting in non-adult interpretations of distributive computation in certain cases. Based on the results, we propose that four- to five-year-old children are less certain about the domain restriction associated with dou-quantification. This proposal has important implications for the current debate on the acquisition of universal quantifiers, specifically, the problem of quantifier spreading. We explain children’s uncertainty about the domain restriction with a universal grammar-based statistical acquisition model.