Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data. Herein, we propose a novel approach for learning such representations from multi-channel EEG time-series, and demonstrate its advantages in the context of mental load classification task. First, we transform EEG activities into a sequence of topology-preserving multi-spectral images, as opposed to standard EEG analysis techniques that ignore such spatial information. Next, we train a deep recurrent-convolutional network inspired by state-of-the-art video classification to learn robust representations from the sequence of images. The proposed approach is designed to preserve the spatial, spectral, and temporal structure of EEG which leads to finding features that are less sensitive to variations and distortions within each dimension. Empirical evaluation on the cognitive load classification task demonstrated significant improvements in classification accuracy over current state-of-the-art approaches in this field.Comment: To be published as a conference paper at ICLR 201

FAILURE, SUCCESS AND LESSONS LEARNED: THE LEGACY OF THE ALGERIAN WAR AND ITS INFLUENCE ON COUNTERINSURGENCY DOCTRINE

The 2003 American invasion of Iraq resulted in a violent insurgency that American forces were initially unable to counter. The United States military was shocked by its failure and was forced to consider what it had done wrong. Once the U.S. military looked into its past it was forced to admit it had wrongly ignored counterinsurgency. To correct this, it assigned many of its officers, along with other military experts, to create a new, updated doctrine that incorporated the lessons of Iraq and other recent, relevant historical precedent. Perhaps surprisingly to some, the United States military interpreted that the Algerian War was of particularly important value. This example, according to the interpretation of the U.S. military, demonstrated certain aspects of counterinsurgency, called \u27laws\u27 by some in the military, that could benefit current world powers. The two aspects of counterinsurgency the U.S. determined were especially important from the Algerian War are the primacy of the population--who must be genuinely convinced to participate on the side of the counterinsurgent force--above all else, including the destruction of the insurgent force and the necessity of the counterinsurgent force to only use methods that are consisted with its stated national ideals. Specifically, the French won the war militarily but still lost politically. This represents an extremely important conclusion for the U.S. military as it has had a history--as in Vietnam--of considering military victory to be the core of its strategy. The Algerian War, according to the American interpretation, was strong evidence that the old way of thinking was no longer possible. Therefore, the U.S. military studied the Algerian War and this \u27lesson\u27 has been directly applied to its current counterinsurgency doctrine. Also, the French use of torture represented another lesson that was particular to the Algerian War. The use of torture in France was of particular interest to the Americans because while it appeared to be working during the Algerian War, the U.S. military interpreted that its success was only a facade. The conspicuous use of torture had undermined French prestige both inside Algeria and around the world. Therefore, even though torture yielded positive, short-term results the long-term result was political failure as France discontinued its effort to retain Algeria. Both of these lessons appear in the current counterinsurgency field manual of the U.S. military, which indicate the direct causal link between the Algerian War and current U.S. counterinsurgency doctrine

Context Attentive Bandits: Contextual Bandit with Restricted Context

We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommender systems and attention modeling. Herein, we adapt the standard multi-armed bandit algorithm known as Thompson Sampling to take advantage of our restricted context setting, and propose two novel algorithms, called the Thompson Sampling with Restricted Context(TSRC) and the Windows Thompson Sampling with Restricted Context(WTSRC), for handling stationary and nonstationary environments, respectively. Our empirical results demonstrate advantages of the proposed approaches on several real-life datasetsComment: IJCAI 201

Analysis of webs of partial-tension-field beams subjected to lateral pressure loadings

Webs of partial-tension-field beams subjected to lateral pressure loading