Thumb-bangers : exploring the cultural bond between video games and heavy metal

« Heavy Metal Generations » is the fourth volume in the series of papers drawn from the 2012 Music, Metal and Politics international conference (http://www.inter-disciplinary.net/publishing/product/heavy-metal-generations/).Heavy metal and video games share an almost simultaneous birth, with Black Sabbath’s debut album in 1970 and Nolan Bushnell’s Computer Space in 1971. From Judas Priest’s ‘Freewheel Burning’ music video in 1984 to Tim Schafer’s Brütal Legend in 2009, the exchanges between these two subcultures have been both reciprocal and exponential. This chapter will present a historical survey of the bond between video games and heavy metal cultures through its highest-profile examples. There are two underlying reasons for this symbiosis: 1) the historical development and popular dissemination of the video game came at an opportune time, first with the video game arcades in the 1970s and early 1980s, and then with the Nintendo Entertainment System, whose technical sound-channel limitations happened to fall in line with the typical structures of heavy metal; 2) heavy metal and video games, along with their creators and consumers, have faced similar sociocultural paths and challenges, notably through the policies set in place by the PMRC and the ESRB, and a flurry of lawsuits and attacks, especially from United States congressmen, that resulted in an overlapping of their respective spaces outside dominant culture. These reasons explain the natural bond between these cultural practices, and the more recent developments like Last Chance to Reason’s Level 2 let us foresee a future where new hybrid creations could emerge

Dual-to-kernel learning with ideals

In this paper, we propose a theory which unifies kernel learning and symbolic algebraic methods. We show that both worlds are inherently dual to each other, and we use this duality to combine the structure-awareness of algebraic methods with the efficiency and generality of kernels. The main idea lies in relating polynomial rings to feature space, and ideals to manifolds, then exploiting this generative-discriminative duality on kernel matrices. We illustrate this by proposing two algorithms, IPCA and AVICA, for simultaneous manifold and feature learning, and test their accuracy on synthetic and real world data.Comment: 15 pages, 1 figur

On Global Warming (Softening Global Constraints)

We describe soft versions of the global cardinality constraint and the regular constraint, with efficient filtering algorithms maintaining domain consistency. For both constraints, the softening is achieved by augmenting the underlying graph. The softened constraints can be used to extend the meta-constraint framework for over-constrained problems proposed by Petit, Regin and Bessiere.Comment: 15 pages, 7 figures. Accepted at the 6th International Workshop on Preferences and Soft Constraint

Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning

Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In the last decade, decision diagrams (DDs) have brought a new perspective on obtaining upper and lower bounds that can be significantly better than classical bounding mechanisms, such as linear relaxations. It is well known that the quality of the bounds achieved through this flexible bounding method is highly reliant on the ordering of variables chosen for building the diagram, and finding an ordering that optimizes standard metrics is an NP-hard problem. In this paper, we propose an innovative and generic approach based on deep reinforcement learning for obtaining an ordering for tightening the bounds obtained with relaxed and restricted DDs. We apply the approach to both the Maximum Independent Set Problem and the Maximum Cut Problem. Experimental results on synthetic instances show that the deep reinforcement learning approach, by achieving tighter objective function bounds, generally outperforms ordering methods commonly used in the literature when the distribution of instances is known. To the best knowledge of the authors, this is the first paper to apply machine learning to directly improve relaxation bounds obtained by general-purpose bounding mechanisms for combinatorial optimization problems.Comment: Accepted and presented at AAAI'1