Are you sitting down? Towards cognitive performance informed design

With many digital interaction designs, we can choose to operate the devices from a variety of postures – what we call self-positioning. In this paper we test two of these choices – sitting vs standing against standard neuropsychological assessments of cognitive executive function. We show that such choices do have significant effects on various cognitive processes. We argue therefore that there is an opportunity to extend parameters of digital interaction design to include self-position in order to optimize that design’s effectiveness for its intended activity

Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients

While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to large, deep neural networks. A central reason is that while random mutation generally works in low dimensions, a random perturbation of thousands or millions of weights is likely to break existing functionality, providing no learning signal even if some individual weight changes were beneficial. This paper proposes a solution by introducing a family of safe mutation (SM) operators that aim within the mutation operator itself to find a degree of change that does not alter network behavior too much, but still facilitates exploration. Importantly, these SM operators do not require any additional interactions with the environment. The most effective SM variant capitalizes on the intriguing opportunity to scale the degree of mutation of each individual weight according to the sensitivity of the network's outputs to that weight, which requires computing the gradient of outputs with respect to the weights (instead of the gradient of error, as in conventional deep learning). This safe mutation through gradients (SM-G) operator dramatically increases the ability of a simple genetic algorithm-based neuroevolution method to find solutions in high-dimensional domains that require deep and/or recurrent neural networks (which tend to be particularly brittle to mutation), including domains that require processing raw pixels. By improving our ability to evolve deep neural networks, this new safer approach to mutation expands the scope of domains amenable to neuroevolution

ES Is More Than Just a Traditional Finite-Difference Approximator

An evolution strategy (ES) variant based on a simplification of a natural evolution strategy recently attracted attention because it performs surprisingly well in challenging deep reinforcement learning domains. It searches for neural network parameters by generating perturbations to the current set of parameters, checking their performance, and moving in the aggregate direction of higher reward. Because it resembles a traditional finite-difference approximation of the reward gradient, it can naturally be confused with one. However, this ES optimizes for a different gradient than just reward: It optimizes for the average reward of the entire population, thereby seeking parameters that are robust to perturbation. This difference can channel ES into distinct areas of the search space relative to gradient descent, and also consequently to networks with distinct properties. This unique robustness-seeking property, and its consequences for optimization, are demonstrated in several domains. They include humanoid locomotion, where networks from policy gradient-based reinforcement learning are significantly less robust to parameter perturbation than ES-based policies solving the same task. While the implications of such robustness and robustness-seeking remain open to further study, this work's main contribution is to highlight such differences and their potential importance

Openness and access

This article uses a data set from the World Economic Forum that quantitatively captures nonexistent or scarce data to test the relationship between trade openness and market access. In recognition of the diversity and range of trade openness measures, this article uses five different openness measures and one measure of market access comprising public institutions, the regulatory environment, and network industries. This article finds that all three components matter and that better market access leads to greater trade openness in both nominal and real terms

Measuring the Accessibility of Arab Markets

Market access matters. This paper uses a method suggested by Hugo et al. (2006) to determine and rank a sample of Arab countries in terms of their market access. The paper suggests that market access is comprised of three components: public institutions, regulatory environment, and network industries. The paper finds that most Arab countries perform better than the world median in terms of market access, except for Morocco and Algeria. The paper demonstrates how these two countries and other Arab countries can improve their market access, either by improving their network industries, their public institutions, or their regulatory environment. Corresponding Author, Zayed University, Economic & Policy Research Unit, P.O. Box 19282, Dubai, UAE, Phone: +971 4 402 1465, Fax: +971 4 402 1002, E-mail: [email protected] Zayed University, Economic & Policy Research Unit, P.O. Box 19282, Dubai, UAE, Phone: +971 4 402 1470, Fax: +971 4 402 1002, E-mail: [email protected]

Large Scale Visualization of Pulsed Vortex Generator Jets

The use of small jets of air has proven to be an effective means of flow control on low Reynolds number turbine blades. Pulsing of these jets has also shown benefits in reducing the amount of air needed to achieve the same level of flow control. An experiment using Hot Wire Anemometry and Particle Image Velocimetry (PIV) has been used to investigate how these pulsed jets interact with the boundary layer to help keep the flow attached. A 25x scaled jet in a flat plate has been utilized. The 25.4 mm diameter jet has a pitch angle of 30° and a skew angle of 90°. Pitch angle is defined as the angle the jet makes with the surface of the plate, and the skew angle is the angle that the projection of the jet on the surface makes with the crossflow. The jet was pulsed at both 0.5 Hz and 4 Hz with varying pulse durations (duty cycles), as well as various blowing ratios (ratio of the jet velocity to the freestream velocity). Duty cycles of 10, 25, 50, and100 percent were implemented at a blowing ratio of unity. Blowing ratios of 0.5, 1, 2, and 4 were implemented at a 50% duty cycle and at 0.5 Hz. Velocity and vorticity planes were obtained at various spanwise locations and used in the characterization of the jetflow. Both the free jet as well as the jet in crossflow were studied. A calibration experiment was also performed using PIV on a rotating disk. The calibration experiment was successful and the PIV results averaged a 1.56% error. The hot wire experiment with the free jet showed that the starting vortex is a key event at the beginning of each cycle, and the end of each cycle included a “kick-back” and a suction effect that could also have an influence on the boundary layer. The PIV experiment was performed first on the free jet, and results were comparable to the hot wire results. When the PIV experiment was performed on the jet in crossflow, it was clear that both the beginning and ending events of the jet cycle were keys to eliminating or delaying flow separation.The effect of the beginning and ending events can be used to keep the flow attached for longer periods of time by increasing the frequency of the jet pulse. Due to limitations of the setup, higher frequency cases could not be studied. However, the experiment was successful in controlling a separated crossflow for blowing ratios greater than unity. The larger blowing ratios resulted in larger attachment size, and were able to sustain attachment for longer time periods

How Open are Arab Economies? An Examination with the CTI Measure

In empirical trade openness studies where trade openness is usually measured as (X+M)/GDP, most Arab countries, particularly larger economies, such as Algeria and Egypt, are determined to be closed to the advantages of world trade. This paper uses a new measure of trade openness, the composite trade intensity (CTI) measure, suggested by Squalli and Wilson (2006) to reconsider the question of Arab country trade openness. The paper suggests that when trade openness is measured using CTI, many Arab economies, particularly the larger ones, are not as closed to the benefits of trade as traditionally thought

Sparse Representations for Fast, One-Shot Learning

Humans rapidly and reliably learn many kinds of regularities and generalizations. We propose a novel model of fast learning that exploits the properties of sparse representations and the constraints imposed by a plausible hardware mechanism. To demonstrate our approach we describe a computational model of acquisition in the domain of morphophonology. We encapsulate phonological information as bidirectional boolean constraint relations operating on the classical linguistic representations of speech sounds in term of distinctive features. The performance model is described as a hardware mechanism that incrementally enforces the constraints. Phonological behavior arises from the action of this mechanism. Constraints are induced from a corpus of common English nouns and verbs. The induction algorithm compiles the corpus into increasingly sophisticated constraints. The algorithm yields one-shot learning from a few examples. Our model has been implemented as a computer program. The program exhibits phonological behavior similar to that of young children. As a bonus the constraints that are acquired can be interpreted as classical linguistic rules

A Computational Model for the Acquisition and Use of Phonological Knowledge

Does knowledge of language consist of symbolic rules? How do children learn and use their linguistic knowledge? To elucidate these questions, we present a computational model that acquires phonological knowledge from a corpus of common English nouns and verbs. In our model the phonological knowledge is encapsulated as boolean constraints operating on classical linguistic representations of speech sounds in term of distinctive features. The learning algorithm compiles a corpus of words into increasingly sophisticated constraints. The algorithm is incremental, greedy, and fast. It yields one-shot learning of phonological constraints from a few examples. Our system exhibits behavior similar to that of young children learning phonological knowledge. As a bonus the constraints can be interpreted as classical linguistic rules. The computational model can be implemented by a surprisingly simple hardware mechanism. Our mechanism also sheds light on a fundamental AI question: How are signals related to symbols