Cascade Of Regions Research Articles

Social contagion induced by uncertain information.

Information and individual activities often spread globally through the network of social ties. While social contagion phenomena have been extensively studied within the framework of threshold models, it is common to make an assumption that may be violated in reality: each individual can observe the neighbors' states without error. Here, we analyze the dynamics of global cascades under uncertainty in an otherwise standard threshold model. Each individual uses statistical inference to estimate the probability distribution of the number of active neighbors when deciding whether to be active, which gives a probabilistic threshold rule. Unlike the deterministic threshold model, the spreading process is generally nonmonotonic, as the inferred distribution of neighbors' states may be updated as a new signal arrives. We find that social contagion may occur as a self-fulfilling event in that misperception may trigger a cascade in regions where cascades would never occur under certainty.

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks

Research at the intersection of computer vision and neuroscience has revealed hierarchical correspondence between layers of deep convolutional neural networks (DCNNs) and cascade of regions along human ventral visual cortex. Recently, studies have uncovered emergence of human interpretable concepts within DCNNs layers trained to identify visual objects and scenes. Here, we asked whether an artificial neural network (with convolutional structure) trained for visual categorization would demonstrate spatial correspondences with human brain regions showing central/peripheral biases. Using representational similarity analysis, we compared activations of convolutional layers of a DCNN trained for object and scene categorization with neural representations in human brain visual regions. Results reveal a brain-like topographical organization in the layers of the DCNN, such that activations of layer-units with central-bias were associated with brain regions with foveal tendencies (e.g. fusiform gyrus), and activations of layer-units with selectivity for image backgrounds were associated with cortical regions showing peripheral preference (e.g. parahippocampal cortex). The emergence of a categorical topographical correspondence between DCNNs and brain regions suggests these models are a good approximation of the perceptual representation generated by biological neural networks.

Foreign Direct Investment Dynamic Performance with Low-Carbon Influence: A Provincial Comparative Application in China

Cross-border capital flows have been a major force driving economic globalization. Foreign direct investment (FDI) plays a decisive role in seeking out market technology brands and enhancing the global competitiveness among international inflows. With the requirement of economic development, this paper focused on a performance evaluation of FDI in China. However, because of the planned transformation to a market economy in China, FDI has been promoted with a regional cascade structure. Similar to the development track of the Chinese economy, it is necessary to evaluate FDI quality more than purely quantity from a provincial point of view. Therefore, this paper evaluated the Chinese provincial FDI total factor productivity using the dynamic Malmquist model. In contrast to traditional evaluations, this paper focused on inter-temporal influence in FDI performance evaluation. To understand the inter-temporal effects, physical capital stock was defined as a dynamic variable in FDI sustainability performance. Additionally, with the pressure to reduce emissions, energy consumption was also considered during the evaluation. The empirical results revealed that the dynamic variable is the bottleneck in FDI performance for most Chinese provinces. It is only efficient in a few municipalities and provinces, such as Shanghai and Guangdong. Additionally, energy conservation was more efficient in the performance evaluation of eastern regions in China.

Automatic detection of referral patients due to retinal pathologies through data mining.

With the increased prevalence of retinal pathologies, automating the detection of these pathologies is becoming more and more relevant. In the past few years, many algorithms have been developed for the automated detection of a specific pathology, typically diabetic retinopathy, using eye fundus photography. No matter how good these algorithms are, we believe many clinicians would not use automatic detection tools focusing on a single pathology and ignoring any other pathology present in the patient's retinas. To solve this issue, an algorithm for characterizing the appearance of abnormal retinas, as well as the appearance of the normal ones, is presented. This algorithm does not focus on individual images: it considers examination records consisting of multiple photographs of each retina, together with contextual information about the patient. Specifically, it relies on data mining in order to learn diagnosis rules from characterizations of fundus examination records. The main novelty is that the content of examination records (images and context) is characterized at multiple levels of spatial and lexical granularity: 1) spatial flexibility is ensured by an adaptive decomposition of composite retinal images into a cascade of regions, 2) lexical granularity is ensured by an adaptive decomposition of the feature space into a cascade of visual words. This multigranular representation allows for great flexibility in automatically characterizing normality and abnormality: it is possible to generate diagnosis rules whose precision and generalization ability can be traded off depending on data availability. A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined. This framework was evaluated in e-ophtha, a dataset of 25,702 examination records from the OPHDIAT screening network, as well as in the publicly-available Messidor dataset. It was successfully applied to the detection of patients that should be referred to an ophthalmologist and also to the specific detection of several pathologies.

Increased precipitation drives mega slump development and destabilization of ice-rich permafrost terrain, northwestern Canada

It is anticipated that an increase in rainfall will have significant impacts on the geomorphology of permafrost landscapes. Field observations, remote sensing and historical climate data were used to investigate the drivers, processes and feedbacks that perpetuate the growth of large retrogressive thaw slumps. These “mega slumps” (5–40ha) are now common in formerly glaciated, fluvially incised, ice-cored terrain of the Peel Plateau, NW Canada. Individual thaw slumps can persist for decades and their enlargement due to ground ice thaw can displace up to 106m3 of materials from slopes to valley bottoms reconfiguring slope morphology and drainage networks. Analysis of Landsat images (1985–2011) indicate that the number and size of active slumps and debris tongue deposits has increased significantly with the recent intensification of rainfall. The analyses of high resolution climatic and photographic time-series for summers 2010 and 2012 shows strong linkages amongst temperature, precipitation and the downslope sediment flux from active slumps. Ground ice thaw supplies meltwater and sediments to the slump scar zone and drives diurnal pulses of surficial flow. Coherence in the timing of down valley debris tongue deposition and fine-scaled observations of sediment flux indicate that heavy rainfall stimulates major mass flow events. Evacuation of sediments from the slump scar zone can help to maintain a headwall of exposed ground ice, perpetuating slump growth and leading to larger disturbances. The development of debris tongue deposits divert streams and increase thermoerosion to initiate adjacent slumps. We conclude that higher rainfall can intensify thaw slump activity and rapidly alter the slope-sediment cascade in regions of ice-cored glaciogenic deposits.

The mechanics of erosion

It is anticipated that an increase in rainfall will have significant impacts on the geomorphology of permafrost landscapes. Field observations, remote sensing and historical climate data were used to investigate the drivers, processes and feedbacks that perpetuate the growth of large retrogressive thaw slumps. These “mega slumps” (5–40 ha) are now common in formerly glaciated, fluvially incised, ice-cored terrain of the Peel Plateau, NW Canada. Individual thaw slumps can persist for decades and their enlargement due to ground ice thaw can displace up to 106 m3 of materials from slopes to valley bottoms reconfiguring slope morphology and drainage networks. Analysis of Landsat images (1985–2011) indicate that the number and size of active slumps and debris tongue deposits has increased significantly with the recent intensification of rainfall. The analyses of high resolution climatic and photographic time-series for summers 2010 and 2012 shows strong linkages amongst temperature, precipitation and the downslope sediment flux from active slumps. Ground ice thaw supplies meltwater and sediments to the slump scar zone and drives diurnal pulses of surficial flow. Coherence in the timing of down valley debris tongue deposition and fine-scaled observations of sediment flux indicate that heavy rainfall stimulates major mass flow events. Evacuation of sediments from the slump scar zone can help to maintain a headwall of exposed ground ice, perpetuating slump growth and leading to larger disturbances. The development of debris tongue deposits divert streams and increase thermoerosion to initiate adjacent slumps. We conclude that higher rainfall can intensify thaw slump activity and rapidly alter the slope-sediment cascade in regions of ice-cored glaciogenic deposits.