Wavelet Shrinkage and Thresholding based Robust Classification for Brain Computer Interface

A macaque monkey is trained to perform two different kinds of tasks, memory aided and visually aided. In each task, the monkey saccades to eight possible target locations. A classifier is proposed for direction decoding and task decoding based on local field potentials (LFP) collected from the prefrontal cortex. The LFP time-series data is modeled in a nonparametric regression framework, as a function corrupted by Gaussian noise. It is shown that if the function belongs to Besov bodies, then using the proposed wavelet shrinkage and thresholding based classifier is robust and consistent. The classifier is then applied to the LFP data to achieve high decoding performance. The proposed classifier is also quite general and can be applied for the classification of other types of time-series data as well, not necessarily brain data

An Empirical Analysis of Dynamic Competitiveness in Africa

Specializing in the export of products made intensively from relatively abundant factors of production and in the import of those made from relatively scarce ones has had a lot of traction in international trade practice. Ample evidence, however, now shows that some nations are gaining a comparative advantage in new areas that they never had before. Consequently, the traditional policy option of many African countries of continuing to specialize in areas in which they already have a comparative advantage (such as exports of primary commodities) is complemented with that of moving to new areas (such as high technology manufacturing) in which they could gain some advantage even though they may not enjoy any at the moment. Trade strategies are therefore now being formulated specifically to transition economies from a state of non-competitiveness to a state of relative competitiveness. This transitioning is what we call dynamic competitiveness in this paper. Empirically, we determine this by analysing the growth of the World Economic Forum’s Global Competitiveness Index of 27 African countries. This is complemented with the analysis of the growth of trade and transport costs using the World Bank Doing Business data. The study finds, among others, that even though the selected Sub-Saharan African countries are improving their (static) competitiveness over time, they cannot be said to be achieving dynamic competitiveness

Learning Linear Groups in Neural Networks

Employing equivariance in neural networks leads to greater parameter efficiency and improved generalization performance through the encoding of domain knowledge in the architecture; however, the majority of existing approaches require an a priori specification of the desired symmetries. We present a neural network architecture, Linear Group Networks (LGNs), for learning linear groups acting on the weight space of neural networks. Linear groups are desirable due to their inherent interpretability, as they can be represented as finite matrices. LGNs learn groups without any supervision or knowledge of the hidden symmetries in the data and the groups can be mapped to well known operations in machine learning. We use LGNs to learn groups on multiple datasets while considering different downstream tasks; we demonstrate that the linear group structure depends on both the data distribution and the considered task