Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI

Automatic brain tumor classification is a practicable means of accelerating clinical diagnosis. Recently, deep convolutional neural network (CNN) training with MRI datasets has succeeded in computer-aided diagnostic (CAD) systems. To further improve the classification performance of CNNs, there is still a difficult path forward with regards to subtle discriminative details among brain tumors. We note that the existing methods heavily rely on data-driven convolutional models while overlooking what makes a class different from the others. Our study proposes to guide the network to find exact differences among similar tumor classes. We first present a “dual suppression encoding” block tailored to brain tumor MRIs, which diverges two paths from our network to refine global orderless information and local spatial representations. The aim is to use more valuable clues for correct classes by reducing the impact of negative global features and extending the attention of salient local parts. Then we introduce a “factorized bilinear encoding” layer for feature fusion. The aim is to generate compact and discriminative representations. Finally, the synergy between these two components forms a pipeline that learns in an end-to-end way. Extensive experiments exhibited superior classification performance in qualitative and quantitative evaluation on three datasets.

Danish satisfaction and Austrian discontent with their governments’ measures during the Covid-19 crisis: Explanations from a citizenship perspective

The various interventions that governments took in the first wave of the Covid-19 outbreak impacted people severely. Given the low satisfaction with the government performance in Austria compared to Denmark, though both governments set out with a suppression strategy early on and were able to lower infection rates, we analyse the changes in civil, political and social citizenship and the governmental communicative practices during the first Covid response phase from March to August 2020. Employing a case-oriented qualitative comparison, we find that a combination of factors explains the different degree of satisfaction. In Austria, there was a combination of politics of fear, extensive and authoritarian regulations of civil citizenship, political citizenship was challenged and social citizenship undermined. In Denmark, an engaging and caring communicative strategy was employed, political citizenship was maintained and civil citizenship was curtailed less obstructively and was less policed. Social citizenship also was upheld for larger groups.

Grid-Connected Resonance Suppression Of Group-Series Photovoltaic Cluster Inverters Based On Hybrid Damping

Grid-connected group-series photovoltaic cluster inverter system will cause resonance, which will adversely affect the system. To suppress grid-connected resonance, the mathematical model, resonance mechanism and resonance characteristics of the cluster inverters are analyzed, and a global resonance suppression strategy based on hybrid damping is proposed. In the current loop of the inverter, capacitive current feedback and parallel voltage proportional feed-forward are introduced as active dampers to reduce the harmonics of the parallel current. On this basis, RLC type second-order resonance suppression circuit is added as passive damping to suppress system resonance, so that the output current of the inverters can meet the grid-connected conditions when the cluster is connected to the grid. The simulation and experimental results show that the total harmonic distortion of the grid-connected current decreases from 10.54% to 1.97% after three series photovoltaic cluster inverters adopt this strategy, which effectively suppresses the grid-connected resonance.