FB-CGANet: filter bank Channel Group Attention network for multi-class motor imagery classification

Objective. Motor imagery-based brain computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography (EEG) signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding. Approach. A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method Channel Group Attention (CGA) to build a lightweight neural network Filter Bank Channel Group Attention Network (FB-CGANet). Accompanied with FB-CGANet, the Band Exchange data augmentation method was proposed to generate training data for networks with filter bank structure. Main results. The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment. Significance. This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.

Optimization of the Oil Drilling Monitoring System Based on the Multisensor Image Fusion Algorithm

The working environment of the oil drilling platform is harsh, with many uncertain factors and high operating risks. During the drilling process, due to sudden formation factors or improper process operations, it is extremely easy to cause well wall instability, sticking, lost circulation, well kick, and blowout. In addition, other complicated situations and accidents have brought major challenges to drilling safety. In order to improve the technical level of oil and gas exploration and development and achieve the goal of reducing costs and increasing efficiency, it is necessary to strengthen the optimization of traditional oil drilling monitoring systems. This article summarizes the advantages and disadvantages of the existing image multiscale analysis algorithms, from wavelet transform, stationary wavelet transforms to contourlet transform, and nondownsampled contours based on the characteristics of the images collected by different sensors in the oil drilling monitoring system and the needs of practical applications. Wave transforms detailed comparison of the fusion performance of these image analysis algorithms under the same fusion rules. Aiming at the shortcoming of the large amount of calculation of nonsubsampled contourlet transform, a fast implementation algorithm (IFNSCT) is proposed. The multichannel filter bank structure is used to replace the original tree filter bank structure, which reduces the time-consuming to the original without affecting the analysis performance of the algorithm. One-half of the oil drilling monitoring efficiency has been improved.

George Kaufman: Both Hands In; Scholar’s Work with Academia and Financial Industry Benefitted Both

This paper examines the career of economist Prof. George Kaufman, whose work combined and benefited both academia and the financial industry, helping both better understand the problems of financial stability, financial regulation, and financial sector competition. In doing so, he brought together great minds from both fields and helped many economists progress in their careers. Prof. Kaufman promulgated the idea that government policy needed to foster two simple goals: proper incentives and competitive markets. He believed the combination would produce a richer society and more stable economy. He created the U.S. Federal Reserve’s Conference on Bank Structure and Competition, the leading conference in the world addressing financial regulatory issues for 50 years. Prof. Kaufman also founded the U.S. Shadow Financial Regulatory Committee in 1986 in the midst of the thrift crisis, helping to solve that crisis and inspiring the creation of other shadow financial regulatory committees around the world. Through his activities, he left behind an outstanding legacy of leadership, research, and opinion in various economic fields, providing platforms upon which many scholars will build in the coming decades.

Distribution of the Macrophyte Communities in the Danube Reflects River Serial Discontinuity

The Danube is the second-longest river in Europe that is subjected to various man-made alterations, including those related to hydro-power plants. We surveyed and analyzed the presence and abundance of macrophytes in the main channel from 2582 river kilometers (rkm) to 171 rkm. We also assessed selected habitat parameters in the sampled river stretches. Sixty-eight different plant species were recorded along the entire course. Among neophytes, we found Elodea nuttallii, E. canadensis, Vallisneriaspiralis and Azolla filiculoides. Based on similarity analysis, we distinguished 15 plant communities, most of which were defined as associations, which were classified into 5 alliances and represented three vegetation classes, namely vegetation of rooted hydrophytes Potamogetonetea, the vegetation of pleustophytes Lemnetea and vegetation of marshes Phragmitetea. The number and abundance of plant species, as well as plant communities recorded in single stretches, varied along the course. Canonical correspondence analysis (CCA) revealed that environmental parameters explained 21% of plant species composition. CCA runs with neophytes explained 41% of the variance, and current velocity, water transparency, species number and bank structure were significant variables. The present study revealed that the free-running sections of the river are poor in number and abundance of plant species, whereas impounded reaches mainly show an opposite result.

MSICA: multi-scale signal decomposition based on independent component analysis with application to denoising and reliable multi-channel

Multi-scale decomposition is a signal description method in which the signal is decomposed into multiple scales, which has been shown to be a valuable method in information preservation. Much focus on multi-scale decomposition has been based on scale-space theory and wavelet transform. In this article, a new powerful method to perform multi-scale decomposition exploiting Independent Component Analysis (ICA), called MSICA, is proposed to translate an original signal into multiple statistically independent scales. It is proven that extracting the independent components of the even and odd samples of a digital signal results in the decomposition of the same into approximation and detail. It is also proven that the whitening procedure in ICA is equivalent to a filter bank structure. Performance results of MSICA in signal denoising are presented; also, the statistical independency of the approximation and detail is exploited to propose a novel signal-denoising strategy for multi-channel noisy transmissions aimed at improving communication reliability by exploiting channel diversity.

Sitting on the Fence: Bank Structural Reforms

The main driver of new, stringent rules on bank structure was not pressure from elected officials, as the two largest UK political parties were ambiguous about the benefits of separating retail and investment banking. Instead, we argue that regulators in the Bank of England pushed strongly for ‘ring-fencing’ to address moral hazard concerns caused by too-big-to-fail banks. Despite fierce opposition from the financial industry, regulators were determined to trade up rules by actively cultivating political support through the Independent Commission on Banking (ICB) and in Parliament. At the international and EU levels, UK regulators acted as fence-sitters on banking reform for two reasons. First, unilateral reform by the US meant that it was not possible to push for an international solution with its traditional ally. Second, resistance to major structural reforms amongst several member states limited the scope for harmonization across the EU.