Marimekko Chart
The Marimekko name has been adopted within business and the management consultancy industry to refer to a bar chart where all the bars are of equal height, there are no spaces between the bars, and the bars are in turn each divided into segments of different width. The design of the ‘marimekko’ chart is said to resemble a Marimekko print. The chart’s design encodes two variables (such as percentage of sales and market share), but it is criticised for making the data hard to perceive and to compare visually. …

Symbolic Reinforcement Learning with Common Sense (SRL+CS)
Deep Reinforcement Learning (deep RL) has made several breakthroughs in recent years in applications ranging from complex control tasks in unmanned vehicles to game playing. Despite their success, deep RL still lacks several important capacities of human intelligence, such as transfer learning, abstraction and interpretability. Deep Symbolic Reinforcement Learning (DSRL) seeks to incorporate such capacities to deep Q-networks (DQN) by learning a relevant symbolic representation prior to using Q-learning. In this paper, we propose a novel extension of DSRL, which we call Symbolic Reinforcement Learning with Common Sense (SRL+CS), offering a better balance between generalization and specialization, inspired by principles of common sense when assigning rewards and aggregating Q-values. Experiments reported in this paper show that SRL+CS learns consistently faster than Q-learning and DSRL, achieving also a higher accuracy. In the hardest case, where agents were trained in a deterministic environment and tested in a random environment, SRL+CS achieves nearly 100% average accuracy compared to DSRL’s 70% and DQN’s 50% accuracy. To the best of our knowledge, this is the first case of near perfect zero-shot transfer learning using Reinforcement Learning. …

Dragon King Theory
The cover of a collection of articles about Dragon Kings Dragon king (DK) is double metaphor for an event that is both extremely large in size or impact (a ‘king’) and born of unique origins (a ‘dragon’) relative to its peers (other events from the same system). DK events are generated by / correspond to mechanisms such as positive feedback, tipping points, bifurcations, and phase transitions, that tend to occur in nonlinear and complex systems, and serve to amplify DK events to extreme levels. By understanding and monitoring these dynamics, some predictability of such events may be obtained. The theory has been developed by Prof. Didier Sornette, who hypothesizes that many of the crises that we face are in fact DK rather than black swans – i.e., they may be predictable to some degree. Given the importance of crises to the long-term organization of a variety of systems, the DK theory urges that special attention be given to the study and monitoring of extremes, and that a dynamic view be taken. From a scientific viewpoint, such extremes are interesting because they may reveal underlying, often hidden, organizing principles. Practically speaking, one should ambitiously study extreme risks, but not forget that significant uncertainty will almost always be present, and should be rigorously considered in decisions regarding risk management and design. The theory of DK is related to concepts such as: black swan theory, outliers, complex systems, nonlinear dynamics, power laws, extreme value theory, prediction, extreme risks, risk management, etc. …

V2CNet
We propose V2CNet, a new deep learning framework to automatically translate the demonstration videos to commands that can be directly used in robotic applications. Our V2CNet has two branches and aims at understanding the demonstration video in a fine-grained manner. The first branch has the encoder-decoder architecture to encode the visual features and sequentially generate the output words as a command, while the second branch uses a Temporal Convolutional Network (TCN) to learn the fine-grained actions. By jointly training both branches, the network is able to model the sequential information of the command, while effectively encodes the fine-grained actions. The experimental results on our new large-scale dataset show that V2CNet outperforms recent state-of-the-art methods by a substantial margin, while its output can be applied in real robotic applications. The source code and trained models will be made available. …