Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. https://www.selleckchem.com/products/PLX-4032.html The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications.This study investigates the effect of different additives, such as coagulants/flocculants, adsorption agents (powdered activated carbon, PAC), and bio-film carriers, on the fouling propensity of a lab-scale membrane bio-reactor (MBR) treating synthetic municipal wastewater. The coagulation agents FO 4350 SSH, Adifloc KD 451, and PAC1 A9-M at concentrations of 10 mg/L, 10 mg/L, and 100 mg Al/L, respectively, and PAC at a concentration of 3.6 ± 0.1 g/L, exhibited the best results during their batch-mode addition to biomass samples. The optimal additives FO 4350 SSH and Adifloc KD 451 were continuously added to the bioreactor at continuous-flow addition experiments and resulted in increased membrane lifetime by 16% and 13%, respectively, suggesting that the decrease of SMPc concentration and the increase of sludge filterability is the dominant fouling reduction mechanism. On the contrary, fouling reduction was low when PAC1 A9-M and PAC were continuously added, as the membrane lifetime was increased by approximately 6%. Interestingly, the addition of bio-film carriers (at filling ratios of 40%, 50%, and 60%) did not affect SMPc concentration, sludge filterability, and trans-membrane pressure (TMP). Finally, the effluent quality was satisfactory in terms of organics and ammonia removal, as chemical oxygen demand (COD), biochemical oxygen demand (BOD)5, and ΝΗ-N concentrations were consistently below the permissible discharge limits and rarely exceeded 30, 15, and 0.9 mg/L, respectively.Image based human behavior and activity understanding has been a hot topic in the field of computer vision and multimedia. As an important part, skeleton estimation, which is also called pose estimation, has attracted lots of interests. For pose estimation, most of the deep learning approaches mainly focus on the joint feature. However, the joint feature is not sufficient, especially when the image includes multi-person and the pose is occluded or not fully visible. This paper proposes a novel multi-task framework for the multi-person pose estimation. The proposed framework is developed based on Mask Region-based Convolutional Neural Networks (R-CNN) and extended to integrate the joint feature, body boundary, body orientation and occlusion condition together. In order to further improve the performance of the multi-person pose estimation, this paper proposes to organize the different information in serial multi-task models instead of the widely used parallel multi-task network. The proposed models are trained on the public dataset Common Objects in Context (COCO), which is further augmented by ground truths of body orientation and mutual-occlusion mask. Experiments demonstrate the performance of the proposed method for multi-person pose estimation and body orientation estimation. The proposed method can detect 84.6% of the Percentage of Correct Keypoints (PCK) and has an 83.7% Correct Detection Rate (CDR). Comparisons further illustrate the proposed model can reduce the over-detection compared with other methods.Elevated distractibility is one of the major contributors to alcohol hangover-induced behavioral deficits. Yet, the basic mechanisms driving increased distractibility during hangovers are still not very well understood. Aside from impairments in attention and psychomotor functions, changes in stimulus-response bindings may also increase responding to distracting information, as suggested by the theory of event coding (TEC). Yet, this has never been investigated in the context of alcohol hangover. Therefore, we investigated whether alcohol hangover has different effects on target-response bindings and distractor-response bindings using a task that allows to differentiate these two phenomena. A total of n = 35 healthy males aged 19 to 28 were tested once sober and once hungover after being intoxicated in a standardized experimental drinking setting the night before (2.64 gr of alcohol per estimated liter of body water). We found that alcohol hangover reduced distractor-response bindings, while no such impairment was found for target-response bindings, which appeared to be unaffected. Our findings imply that the processing of distracting information is most likely not increased, but in fact decreased by hangover. This suggests that increased distractibility during alcohol hangover is most likely not caused by modulations in distractor-response bindings.Cerebellar ataxias are a heterogenous group of degenerative disorders for which we currently lack effective and disease-modifying interventions. The field of non-invasive brain stimulation has made much progress in the development of specific stimulation protocols to modulate cerebellar excitability and try to restore the physiological activity of the cerebellum in patients with ataxia. In light of limited evidence-based pharmacologic and non-pharmacologic treatment options for patients with ataxia, several different non-invasive brain stimulation protocols have emerged, particularly employing repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) techniques. In this review, we summarize the most relevant rTMS and tDCS therapeutic trials and discuss their implications in the care of patients with degenerative ataxias.