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

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

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

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

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

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]

Mems device with large out-of-plane actuation and low-resistance interconnect and methods of use

Source: United States Patent and Trademark Office, www.uspto.gov”The present application is directed to a MEMS device. The MEMS device includes a substrate having a first end and a second end extending along a longitudinal axis, the Substrate including an electrostatic actuator. The device also includes a movable plate having a first end and a second end. The device also includes a thermal actuator having a first end coupled to the first end of the substrate and a second end coupled to the first end of the plate. The actuator moves the plate in relation to the substrate. Further, the device includes a power source electrically coupled to the thermal actuator and the Substrate. The application is also directed to a method for operating a MEMS device

Atmospheric Environmental Safety Technologies Project Atmospheric Hazard Safety Mitigation: Lightning and EM Effects Mitigation

This viewgraph presentation describes various lightning strike and electromagnetic sensing mitigation technologies to minimize flight safety risks

A Comparison of Photocatalytic Oxidation Reactor Performance for Spacecraft Cabin Trace Contaminant Control Applications

Photocatalytic oxidation (PCO) is a maturing process technology that shows potential for spacecraft life support system application. Incorporating PCO into a spacecraft cabin atmosphere revitalization system requires an understanding of basic performance, particularly with regard to partial oxidation product production. Four PCO reactor design concepts have been evaluated for their effectiveness for mineralizing key trace volatile organic com-pounds (VOC) typically observed in crewed spacecraft cabin atmospheres. Mineralization efficiency and selectivity for partial oxidation products are compared for the reactor design concepts. The role of PCO in a spacecraft s life support system architecture is discussed

Analysis of margin classification systems for assessing the risk of local recurrence after soft tissue sarcoma resection

Purpose: To compare the ability of margin classification systems to determine local recurrence (LR) risk after soft tissue sarcoma (STS) resection. Methods: Two thousand two hundred seventeen patients with nonmetastatic extremity and truncal STS treated with surgical resection and multidisciplinary consideration of perioperative radiotherapy were retrospectively reviewed. Margins were coded by residual tumor (R) classification (in which microscopic tumor at inked margin defines R1), the R+1mm classification (in which microscopic tumor within 1 mm of ink defines R1), and the Toronto Margin Context Classification (TMCC; in which positive margins are separated into planned close but positive at critical structures, positive after whoops re-excision, and inadvertent positive margins). Multivariate competing risk regression models were created. Results: By R classification, LR rates at 10-year follow-up were 8%, 21%, and 44% in R0, R1, and R2, respectively. R+1mm classification resulted in increased R1 margins (726 v 278, P < .001), but led to decreased LR for R1 margins without changing R0 LR; for R0, the 10-year LR rate was 8% (range, 7% to 10%); for R1, the 10-year LR rate was 12% (10% to 15%) . The TMCC also showed various LR rates among its tiers (P < .001). LR rates for positive margins on critical structures were not different from R0 at 10 years (11% v 8%, P = .18), whereas inadvertent positive margins had high LR (5-year, 28% [95% CI, 19% to 37%]; 10-year, 35% [95% CI, 25% to 46%]; P < .001). Conclusion: The R classification identified three distinct risk levels for LR in STS. An R+1mm classification reduced LR differences between R1 and R0, suggesting that a negative but < 1-mm margin may be adequate with multidisciplinary treatment. The TMCC provides additional stratification of positive margins that may aid in surgical planning and patient education